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Spectroscopic profiling data (e.g., Raman spectroscopy and mass spectroscopy), combined with machine learning, have provided a data-driven approach for discriminative tasks. In these tasks, researchers often start with simple classification models. If one model doesn’t work, they will try more sophisticated models. If all models fail, the researchers will deem the data set as “inseparable.“ This “trial-and-error” practice reveals a fundamental question: does the dataset possess the necessary statistical power for the current discriminative task? This “classifiability analysis” is an implicit and often neglected step in the data-driven pipeline. This paper aims to design a unified methodological framework for classifiability analysis. In this framework, a meta-learner model combines diversified atom metrics (e.g., Bayes error rate / irreducible error, classification accuracy, information gain / mutual information) into one unified metric (d). We have successfully used the proposed framework to analyze a spectroscopic profiling dataset to discriminate vintage liquors of different ages. A significant difference (d = 1.447. d > 0.8 indicates a significant difference) between 5-year and 16-year liquors.

At present, the traditional scoring methods generally utilize laboratory measurements to predict mortality. It results in difficulties of early mortality prediction in the rural areas lack of professional laboratorians and medical laboratory equipment. To improve the efficiency, accuracy, and applicability of mortality prediction in the remote areas, a novel mortality prediction method based on machine learning algorithms is proposed, which only uses non-invasive parameters readily available from ordinary monitors and manual measurement. A new feature selection method based on the Bayes error rate is developed to select valuable features. Based on non-invasive parameters, four machine learning models were trained for early mortality prediction. The subjects contained in this study suffered from general critical diseases including but not limited to cancer, bone fracture, and diarrhea. Comparison tests among five traditional scoring methods and these four machine learning models with and without laboratory measurement variables are performed. Only using the non-invasive parameters, the LightGBM algorithms have an excellent performance with the largest accuracy of 0.797 and AUC of 0.879. There is no apparent difference between the mortality prediction performance with and without laboratory measurement variables for the four machine learning methods. After reducing the number of feature variables to no more than 50, the machine learning models still outperform the traditional scoring systems, with AUC higher than 0.83. The machine learning approaches only using non-invasive parameters achieved an excellent mortality prediction performance and can equal those using extra laboratory measurements, which can be applied in rural areas and remote battlefield for mortality risk evaluation. Graphical abstract.

In the context of supervised learning, meta learning uses features, metadata and other information to learn about the difficulty, behavior, or composition of the problem. Using this knowledge can be useful to contextualize classifier results or allow for targeted decisions about future data sampling. In this paper, we are specifically interested in learning the Bayes error rate (BER) based on a labeled data sample. Providing a tight bound on the BER that is also feasible to estimate has been a challenge. Previous work[1] has shown that a pairwise bound based on the sum of Henze-Penrose (HP) divergence over label pairs can be directly estimated using a sum of Friedman-Rafsky (FR) multivariate run test statistics. However, in situations in which the dataset and number of classes are large, this bound is computationally infeasible to calculate and may not be tight. Other multi-class bounds also suffer from computationally complex estimation procedures. In this paper, we present a generalized HP divergence measure that allows us to estimate the Bayes error rate with log-linear computation. We prove that the proposed bound is tighter than both the pairwise method and a bound proposed by Lin [2]. We also empirically show that these bounds are close to the BER. We illustrate the proposed method on the MNIST dataset, and show its utility for the evaluation of feature reduction strategies. We further demonstrate an approach for evaluation of deep learning architectures using the proposed bounds., 15 pages, 12 figures, 1 table

The Bayes classification rule offers the optimal classifier, minimizing the classification error rate, whereas the Neyman–Pearson lemma offers the optimal family of classifiers to maximize the detection rate for any given false alarm rate. These motivate studies on comparing classifiers based on similarities between the classifiers and the optimal. In this article, we define partial order relations on classifiers and families of classifiers, based on rankings of rate function values and rankings of test function values, respectively. Each partial order relation provides a sufficient condition, which yields better classification error rates or better performance on the receiver operating characteristic analysis. Various examples and applications of the partial order theorems are discussed to provide comparisons of classifiers and families of classifiers, including the comparison of cross‐validation methods, training data that contains outliers, and labelling errors in training data. The Canadian Journal of Statistics 48: 152–166; 2020 © 2019 Statistical Society of Canada

This paper addresses the spectrum sensing problem in an orthogonal frequency-division multiplexing (OFDM) system based on machine learning. To adapt to signal-to-noise ratio (SNR) variations, we first formulate the sensing problem into a novel SNR-related multi-class classification problem. Then, we train a naive Bayes classifier (NBC), and propose a class-reduction assisted prediction method to reduce spectrum sensing time. We derive the performance bounds by translating the Bayes error rate into spectrum sensing error rate. Compared with the conventional methods, the proposed method is shown by simulation to achieve higher spectrum sensing accuracy, in particular at critical areas of low SNRs. It offers a potential solution to the hidden node problem.

Feature selection has become a crucial part of many classification problems in which high-dimensional datasets may contain tens of thousands of features. In this paper, we propose a novel feature selection method scoring the features through estimating the Bayes error rate based on kernel density estimation. Additionally, we update the scores of features dynamically by quantitatively interpreting the effects of feature relevance and redundancy in a new way. Distinguishing from the common heuristic applied by many feature selection methods, which prefers choosing features that are not relevant to each other, our approach penalizes only monotonically correlated features and rewards any other kind of relevance among features based on Spearman's rank correlation coefficient and normalized mutual information. We conduct extensive experiments on seventeen diverse classification benchmarks and the results show that our approach overperforms other seventeen popular state-of-the-art feature selection methods in most cases.

This paper examines the implications of the market selection hypothesis on the accuracy of the probabilities implied by equilibrium prices and on the “learning” mechanism of markets. I use the standard machinery of dynamic general equilibrium models to generate a rich class of probabilities, price probabilities, and discuss their properties. This class includes the Bayes’ rule and known non-Bayesian rules. If the prior support is well-specified, I prove that all members of this class perform as well as Bayes’ rule in terms of likelihood. If the prior support is misspecified in that the Bayesian prior does not converge, I demonstrate that some members of price probabilities significantly outperform Bayes’. Because these members are never worse and sometimes better than Bayes, my result challenges the prevailing opinion that Bayes’ rule is the only rational way to learn.

This study wishes to apply a non-parametric estimate of the Bayes Error Rate (BER) to current Air Force problems related to target classification. Whether they be neural networks, autoencoders, or other architectures, classifiers are commonly assessed through confusion matrices and associated statistics, or visualizations of feature spaces like t-SNE plots. However, these methods depend on the test data used to assess the performance of the network, not the robustness of the classifier itself. This research incorporates a different statistic that estimates the BER, or the probability of misclassification given some data, to serve as an upper bound for potential classifier performance. This estimate leverages a Friedman-Rafsky test statistic: the number of cross labels in a minimum-spanning tree (MST) through points in the feature space. The first part of this study examines the behavior of the BER estimate over a general learning process, such as different epochs of the training process in a neural network. The second part of the study examines whether certain factors affect the separability of synthetic aperture radar (SAR) images of targets of interest. Given the fact that it is often difficult and expensive to survey real targets and generate SAR images, 3-D CAD models are frequently used to generate synthetic SAR images. However, given that many resources are devoted to perfecting these models, this study applies the BER estimate to examine whether minute changes to CAD models affect separability in the image domain. The results seem to indicate that, if the topology of the target is maintained in the CAD domain, low-fidelity versions of targets (with 25% the number of faces of highly accurate models), exhibit separability and ability to be correctly classified identical to their high-fidelity counterparts. The BER estimation also shows promising applications in other domains, serving as a way to describe the underlying structure of feature spaces intuitively but effectively.

We propose a supervised linear dimensionality reduction algorithm.The algorithm reduces the dimensionality of data to K1 for the K-class problem.The linearly reduced data is well-suited for Bayesian classification.Experiments on UCI datasets are performed for the proposed and existing algorithms.The applicability of the algorithm to flow meter diagnostics is also demonstrated. Supervised linear dimensionality reduction (LDR) performed prior to classification often improves the accuracy of classification by reducing overfitting and removing multicollinearity. If a Bayes classifier is to be used, then reduction to a dimensionality of K1 is necessary and sufficient to preserve the classification information in the original feature space for the K-class problem. However, most of the existing algorithms provide no optimal dimensionality to which to reduce the data, thus classification information can be lost in the reduced space if K1 dimensions are used. In this paper, we present a novel LDR technique to reduce the dimensionality of the original data to K1, such that it is well-primed for Bayesian classification. This is done by sequentially constructing linear classifiers that minimise the Bayes error via a gradient descent procedure, under an assumption of normality. We experimentally validate the proposed algorithm on 10 UCI datasets. Our algorithm is shown to be superior in terms of the classification accuracy when compared to existing algorithms including LDR based on Fishers criterion and the Chernoff criterion. The applicability of our algorithm is then demonstrated by employing it in diagnosing the health states of 2 ultrasonic flow meters. As with the UCI datasets, the proposed algorithm is found to have superior performance to the existing algorithms, achieving classification accuracies of 99.4% and 97.5% on the two flow meters. Such high classification accuracies on the flow meters promise significant cost benefits in oil and gas operations.

예이츠의 작품은 다시 찾은 시공과 재창조된 시공에 존재한다. 창조된 시공은 현대시대에서 음유시인의 역할을 재상상함으로써 자신을 위해 만든 것이다. 1937년 환상록을 완성함으로써 그의 작품은 전통적인 영원성의 시간개념(일순간 인식된 모든 시간)을 반영하기 시작할 뿐 아니라, 『창유리에 쓰인 말』과 『연옥』에서 보이는 현세적인 시간과 영원한 시간 사이에 존재하는 초자연적 시간도 반영한다. 이 초자연적 시간은 보통 우리가 살아가는 시간보다 더 유동적이다.

The Naive Bayes (NB) classifier is a popular classifier for text classification problems due to its simple, flexible framework and its reasonable performance. In this paper, we present how to effectively utilize negative class information to improve NB classification. As opposed to information retrieval, supervised learning based text classification already obtains class information, a negative class as well as a positive class, from a labeled training dataset. Since the negative class can also provide significant information to improve the NB classifier, the negative class information is applied to the NB classifier through two phases of indexing and class prediction tasks. As a result, the new classifier using the negative class information consistently performs better than the traditional multinomial NB classifier.

We proposed an approach to error recovery that uses the concepts of task stratification and error classification. In our method, errors are classified according to the estimated cause into several categories, such as modeling and planning errors. When an error is classified correctly, this increases the probability that the most suitable recovery is performed. In this paper, we describe a procedure for error categorization.

We derive a linear classifier for heteroscedastic linear discriminant analysis.The proposed scheme efficiently minimises the Bayes error for binary classification.A local neighbourhood search is also proposed for non-normal distributions.The proposed schemes are experimentally validated on twelve datasets. Under normality and homoscedasticity assumptions, Linear Discriminant Analysis (LDA) is known to be optimal in terms of minimising the Bayes error for binary classification. In the heteroscedastic case, LDA is not guaranteed to minimise this error. Assuming heteroscedasticity, we derive a linear classifier, the Gaussian Linear Discriminant (GLD), that directly minimises the Bayes error for binary classification. In addition, we also propose a local neighbourhood search (LNS) algorithm to obtain a more robust classifier if the data is known to have a non-normal distribution. We evaluate the proposed classifiers on two artificial and ten real-world datasets that cut across a wide range of application areas including handwriting recognition, medical diagnosis and remote sensing, and then compare our algorithm against existing LDA approaches and other linear classifiers. The GLD is shown to outperform the original LDA procedure in terms of the classification accuracy under heteroscedasticity. While it compares favourably with other existing heteroscedastic LDA approaches, the GLD requires as much as 60 times lower training time on some datasets. Our comparison with the support vector machine (SVM) also shows that, the GLD, together with the LNS, requires as much as 150 times lower training time to achieve an equivalent classification accuracy on some of the datasets. Thus, our algorithms can provide a cheap and reliable option for classification in a lot of expert systems.

The Naive Bayes approximation (NBA) and associated classifier are widely used and offer robust performance across a large spectrum of problem domains. As it depends on a very strong assumption--independence among features--this has been somewhat puzzling. Various hypotheses have been put forward to explain its success and many generalizations have been proposed. In this paper we propose a set of "local" error measures--associated with the likelihood functions for subsets of attributes and for each class--and show explicitly how these local errors combine to give a "global" error associated to the full attribute set. By so doing we formulate a framework within which the phenomenon of error cancelation, or augmentation, can be quantified and its impact on classifier performance estimated and predicted a priori. These diagnostics allow us to develop a deeper and more quantitative understanding of why the NBA is so robust and under what circumstances one expects it to break down. We show how these diagnostics can be used to select which features to combine and use them in a simple generalization of the NBA, applying the resulting classifier to a set of real world data sets.

Linear discriminant analysis (LDA) is a well-known classification technique that enjoyed great success in practical applications. Despite its effectiveness for traditional low-dimensional problems, extensions of LDA are necessary in order to classify high-dimensional data. Many variants of LDA have been proposed in the literature. However, most of these methods do not fully incorporate the structure information among predictors when such information is available. In this paper, we introduce a new high-dimensional LDA technique, namely graph-based sparse LDA (GSLDA), that utilizes the graph structure among the features. In particular, we use the regularized regression formulation for penalized LDA techniques, and propose to impose a structure-based sparse penalty on the discriminant vector β . The graph structure can be either given or estimated from the training data. Moreover, we explore the relationship between the within-class feature structure and the overall feature structure. Based on this relationship, we further propose a variant of our proposed GSLDA to utilize effectively unlabeled data, which can be abundant in the semi-supervised learning setting. With the new regularization, we can obtain a sparse estimate of β and more accurate and interpretable classifiers than many existing methods. Both the selection consistency of β estimation and the convergence rate of the classifier are established, and the resulting classifier has an asymptotic Bayes error rate. Finally, we demonstrate the competitive performance of the proposed GSLDA on both simulated and real data studies.

Recent experimental studies have shown that the support vector machine and the random forest are "stand out" models that outperform most other supervised learning methods in benchmarked evaluations on real-world data sets. Studies have also established that both these models are closely related to nearest neighbor classifiers. Due to this connection, one would expect a similar performance from a nearest neighbor classifier; however, the latter lags greatly behind the support vector machine and the random forest in most evaluations. On the contrary, the nearest neighbor classifier has great theoretical potential because of its ability to model complex decision boundaries with low bias. Furthermore, it theoretically guarantees an error of no more than twice the Bayes error rate on an infinite data set. In this paper, we examine this obvious disconnect between the great theoretical promise and empirical reality of the nearest neighbor classifier. An additional point is that the random forest and support vector machine can be considered eager forms of the (lazy) nearest neighbor classifier. Inspired by the connections between nearest neighbor classifiers, support vector machines and random forest models, we propose a nearest neighbor classifier that is competitive to these models.

Objectives: The time complexity of the machine learning algorithm is directly proportionate to the dimension of the dataset. In this paper, he impacts of dimensionality of the dataset on the machine learning algorithm, Naive-Bayes Classifier is evaluated with all feature subsets to analyze whether there is any variations in the performance. Methods/Statistical Analysis: Naive Bayes Classifier is taken for the study to evaluate its variations in terms of its performance in correctly classified instances and incorrectly classified instances. Pima Indian Type II diabetes dataset is taken for the experimental study. Confusion matrix will be formulated for the performance of Naive-Bayes Classifier using 10-fold cross validation for each run. The study exhibits the impact of the dimensionality on the performance of Naive-Bayes Classifier. Findings: The Naive Bayes classifier classifies the patient records either as diabetes or as non-diabetes using the values of the feature set. It is a probabilistic approach of classifying the patient records into the binary class. It is found that there is an impact on the performance of Naive Bayes Classifier due to the dimensionality of the feature set it terms of Classification accuracy, number of true positives, true negatives, false positives and false negatives. The incorrect classification is certainly dangerous. Whereas the valid classification facilitates the healthcare systems in terms of planning effective course of treatment which will save the life of the patient. The invalid classification will lead to a wrong diagnosis while formulating the treatment plan and it will lead to loss of life. Hence, the invalid classification in terms of false negative rate is to be viewed very seriously. In this paper, the study shows that there is an impact on the performance of Naive Bayes Classifier due to the higher dimensionality of the dataset. Application/Improvements: They will be used in medical Informatics for the quality diagnosis and effective treatment planning. The focus on the false positive rate in the classification accuracy of Naive Bayes Classifier will notably help the healthcare systems to diagnose the patients accurately to save life.

Bayes linear kinematics and Bayes linear Bayes graphical models provide an extension of Bayes linear methods so that full conditional updates may be combined with Bayes linear belief adjustment. In this paper we investigate the application of this approach to survival analysis with time-dependent covariate effects, a more complicated problem than previous applications. We use a piecewise-constant hazard function with a prior in which covariate effects are correlated over time. The need for computationally intensive methods is avoided and the relatively simple structure facilitates interpretation. Our approach eliminates the problem of non-commutativity which was observed in earlier work by Gamerman. We apply the technique to data on survival times for leukemia patients. We demonstrate the use Bayes linear kinematics in a more complicated problem.We use a realistically complicated case study in semi-parametric survival analysis.The previously-observed problem of non-commutativity is overcome.Attention is given to the specification of prior beliefs and its effects.

Defining notion of Bayes covariance.Proving existence and uniqueness of Bayes covariant classifier of 3 categories.Explicit construction of a Bayes covariant classifier for any number of categories.A proof that previously considered methods are not Bayes covariant.Comparison of various methods for combining pairwise classifiers via MDS, and speech frame classification. We consider multi-class classification models built from complete sets of pairwise binary classifiers. The BradleyTerry model is often used to estimate posterior distributions in this setting. We introduce the notion of Bayes covariance, which holds if the multi-class classifier respects multiplicative group action on class priors. As a consequence, a Bayes covariant method yields the same result whether new priors are considered before or after combination of the individual classifiers, which has several practical advantages for systems with feedback. In the paper, we construct a Bayes covariant combining method and compare it with previously published methods in both Monte Carlo simulations as well as on a practical speech frame recognition task.

Text classification is the task of assigning predefined categories to natural language documents, and it can provide conceptual views of document collections. The Naïve Bayes (NB) classifier is a family of simple probabilistic classifiers based on a common assumption that all features are independent of each other, given the category variable, and it is often used as the baseline in text classification. However, classical NB classifiers with multinomial, Bernoulli and Gaussian event models are not fully Bayesian. This study proposes three Bayesian counterparts, where it turns out that classical NB classifier with Bernoulli event model is equivalent to Bayesian counterpart. Finally, experimental results on 20 newsgroups and WebKB data sets show that the performance of Bayesian NB classifier with multinomial event model is similar to that of classical counterpart, but Bayesian NB classifier with Gaussian event model is obviously better than classical counterpart.

Diabetes can lead to mortality and disability, so patients should be inpatient again to undergo treatment again to be saved. On previous research about feature selection with greedy stepwise forward fail to predict classification ratio inpatient of patient with the result of recall and precision 0 on data training 60%, 75%, 80%, and 90% and there is suggestion to handle unbalanced class data problem by comparison of data readmitted 6293 and the otherwise 64141. The research purposed to know the effect of choosing the best model using best first instead of greedy stepwise forward and data sampling with spreadsubsample to resolve unbalanced class data problem. The data used was patient data from 130 American Hospital in 1999 until 2008 with 70434 data. The method that used was best first search and spreadsubsample. The result of this research are precision found 0.4 and 0.333 on training dataset 75% and 90% with best first method, while spreadsubsample method found that value of precision and recall is more significantly increased. Spreadsubsample has more effect with the result of precision and recall rather than using best first method.

Semi-supervised algorithms are well-known for their ability to combine both supervised and unsupervised strategies for optimizing their learning ability under the assumption that only a few examples together with their full feature set are given. In such cases, the use of weak learners as base classifiers is usually preferred, since the iterative behavior of semi-supervised schemes require the building of new temporal models during each new iteration. Locally weighted naive Bayes classifier is such a classifier that encompasses the power of NB and k-NN algorithms. In this work, we have implemented a self-labeled weighted variant of local learner which uses NB as the base classifier of self-training scheme. We performed an in depth comparison with other well-known semi-supervised classification methods on standard benchmark datasets and we reached to the conclusion that the presented technique had better accuracy in most cases.

Background Electroencephalogram (EEG) and electromyogram (EMG) recordings are often used in rodents to study sleep architecture and sleep-associated neural activity. These recordings must be scored to designate what sleep/wake state the animal is in at each time point. Manual sleep-scoring is very time-consuming, so machine-learning classifier algorithms have been used to automate scoring. New method Instead of using single classifiers, we implement a multiple classifier system. The multiple classifier is built from six base classifiers: decision tree, k-nearest neighbors, naive Bayes, support vector machine, neural net, and linear discriminant analysis. Decision tree and k-nearest neighbors were improved into ensemble classifiers by using bagging and random subspace. Confidence scores from each classifier were combined to determine the final classification. Ambiguous epochs can be rejected and left for a human to classify. Results Support vector machine was the most accurate base classifier, and had error rate of 0.054. The multiple classifier system reduced the error rate to 0.049, which was not significantly different from a second human scorer. When 10% of epochs were rejected, the remaining epochs’ error rate dropped to 0.018. Comparison with existing method(s) Compared with the most accurate single classifier (support vector machine), the multiple classifier reduced errors by 9.4%. The multiple classifier surpassed the accuracy of a second human scorer after rejecting only 2% of epochs. Conclusions Multiple classifier systems are an effective way to increase automated sleep scoring accuracy. Improvements in autoscoring will allow sleep researchers to increase sample sizes and recording lengths, opening new experimental possibilities.

this paper investigation of the performance criterion of a machine learning tool, Naive Bayes Classifier with a new weighted approach in classifying breast cancer is done . Naive Bayes is one of the most effective classification algorithms. In many decision making system, ranking performance is an interesting and desirable concept than just classification. So to extend traditional Naive Bayes, and to improve its performance, weighted concept is incorporated. Exploration of Domain knowledge based weight assignment on UCI machine learning repository dataset of breast cancer is performed. As Breast cancer is considered to be second leading cause of death in women today. The experiments show that a weighted naive bayes approach outperforms naive bayes. KeywordsMining, Breast cancer, Naive bayes classifier, Domain based weight, Weights, Posterior probability, UCI machine learning repository, Prediction.

Image Classification is important step in Image retrieval. Multimedia Classification gaining importance due to increase in number of application. Many approaches has been used for image classification. This paper proposes Image Classification approach using LBG vector quantization method with Bayes and Lazy family data mining classifiers. Here Experimentation is done using 6 data mining classifier and 7 codebook sizes. Total 42 variations are performed on a Test database. 500 images is used as an Image set to compute the classification accuracy. 74.77%performance is observed by Bayes Net classifier of Bayes Family with 128 Codebook size of LBG.

The classification of heart disease patients is of great importance in cardiovascular disease diagnosis. Numerous data mining techniques have been used so far by the researchers to aid health care professionals in the diagnosis of heart disease. For this task, many algorithms have been proposed in the previous few years. In this paper, we have studied different supervised machine learning techniques for classification of heart disease data and have performed a procedural comparison of these. We have used the C4.5 decision tree classifier, a naïve Bayes classifier, and a Support Vector Machine (SVM) classifier over a large set of heart disease data. The data used in this study is the Cleveland Clinic Foundation Heart Disease Data Set available at UCI Machine Learning Repository. We have found that SVM outperformed both naïve Bayes and C4.5 classifier, giving the best accuracy rate of correctly classifying highest number of instances. We have also found naïve Bayes classifier achieved a competitive performance though the assumption of normality of the data is strongly violated.Bangladesh J. Sci. Ind. Res. 50(4), 293-296, 2015

In this study, an Aggregated Fuzzy Naive Bayes Classifier is proposed for decision-making problems where both linguistic and numerical information are available. In the solution process of such problems, all attributes are considered as fuzzy numbers and a procedure based on 2-tuple fuzzy linguistic representation model is generated for combining them. This procedure and subsequent Fuzzy Naive Bayes classification are performed based on arithmetic operations defined by Chen's function principle. The proposed method was demonstrated on 2 well-known examples from the literature in which both numerical and linguistic attributes were considered. The results show that the proposed Aggregated Fuzzy Naive Bayes Classifier is notably efficient in decision-making where the attributes are in more realistic forms.

The practical limitation of the Bayes classifier used in pattern recognition is computing the class-conditional probability density function (pdf) of the vectors belonging to the corresponding classes. In this paper, a neural network based approach is proposed to model the class-conditional pdf, which can be further used in supervised classifier (e.g. Bayes classifier). It is also suggested to use kernel version (using kernel trick) of the proposed approach to obtain the class-conditional pdf of the corresponding training vectors in the higher dimensional space. This is used for better class separation and hence better classification rate is achieved. The performance of the proposed technique is validated by using the synthetic data and the real data. The simulation results show that the proposed technique on synthetic data and the real data performs well (in terms of classification accuracy) when compared with the classical Fisher?s Linear Discriminant Analysis (LDA) and Gaussian based Kernel-LDA.

Clustering algorithms typically group points based on some similarity criterion, but without reference to an underlying random process to make clustering algorithms rigorously predictive. In fact, there exists a probabilistic theory of clustering in the context of random labeled point sets in which clustering error is defined in terms of the process. In the present paper, given an underlying point process we develop a general analytic procedure for finding an optimal clustering operator, the Bayes clusterer, that corresponds to the Bayes classifier in classification theory. We provide detailed solutions under Gaussian models. Owing to computational complexity we also develop approximations of the Bayes clusterer. Fil: Dalton, Lori A.. Ohio State University; Estados Unidos Fil: Benalcazar Palacios, Marco Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina Fil: Brun, Marcel. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina Fil: Dougherty, Edward R.. Texas A&M University; Estados Unidos

This paper focuses on comparative study of calculation of classification error with classical PCA technique and our adaptive method. Frontal face image database with uniform lightening condition and color images with different orientations have taken in order to get better accuracy in the result. Conventional PCA algorithm is applied for dimensional reduction and calculating the classification error. The result generated from this technique is being compared with the result generated from our adaptive method Naive Bayes Classifier. We have used Naive Bayes Classifier for calculating the classification error of each feature vector instead of considering K largest Eigen-value as in PCA. A covariance matrix is arranged by considering the feature vector having the lowest K minimum error. We have applied the adaptive technique on 626 colored facial images with uniform illumination conditions and varying poses and 545 frontal facial images with uniform background to get the better accuracy.

The subject of this paper is human error as cause of technical and organizational system failure. The risk analysis, as important part of the modern project and processes management, focus increasing attention to the study of human reliability and human error. Human error is considered as any activity outside the tolerance limits which are predefined by the system, formally or informally. Besides a review of different approaches and methods for human errors analysis, the Human Factors Analysis and Classification System (HFACS) method will be presented in detail. This relatively new method for the analysis of human errors causes will be illustrated using a real life example.

The problem of classifying a spatial multivariate Gaussian data into one of several categories specified by different regression mean models is considered. The classifier based on plug-in Bayes classification rule (PBCR) formed by replacing unknown parameters in Bayes classification rule (BCR) with category parameters estimators is investigated. This is the extension of the previous one from the two category case to the multiple category case. The novel close-form expressions for the Bayes misclassification probability and actual error rate associated with PBCR are derived. These error rates are suggested as performance measures for the classifications procedure. The three-category case with feature modelled by bivariate stationary Gaussian random field on regular lattice with exponential covariance function is used for the numerical analysis. Dependence of the derived error rates on category parameters is studied.

This paper discusses the problem of classifying a multivariate Gaussian random field observation into one of the several categories specified by different parametric mean models. Investigation is conducted on the classifier based on plug-in Bayes classification rule (PBCR) formed by replacing unknown parameters in Bayes classification rule (BCR) with category parameters estimators. This is the extension of the previous one from the two category cases to the multi-category case. The novel closed-form expressions for the Bayes classification probability and actual correct classification rate associated with PBCR are derived. These correct classification rates are suggested as performance measures for the classifications procedure. An empirical study has been carried out to analyze the dependence of derived classification rates on category parameters.

Nowadays, surface defect detection systems for steel strip have replaced traditional artificial inspection systems, and automatic defect detection systems offer good performance when the sample set is large and the model is stable. However, the trained model does work well when a new production line is initiated with different equipment, processes, or detection devices. These variables make just tiny changes to the real-world model but have a significant impact on the classification result. To overcome these problems, we propose an evolutionary classifier with a Bayes kernel (BYEC) that can be adjusted with a small sample set to better adapt the model for a new production line. First, abundant features were introduced to cover detailed information about the defects. Second, we constructed a series of support vector machines (SVMs) with a random subspace of the features. Then, a Bayes classifier was trained as an evolutionary kernel fused with the results from the sub-SVM to form an integrated classifier. Finally, we proposed a method to adjust the Bayes evolutionary kernel with a small sample set. We compared the performance of this method to various algorithms; experimental results demonstrate that the proposed method can be adjusted with a small sample set to fit the changed model. Experimental evaluations were conducted to demonstrate the robustness, low requirement for samples, and adaptiveness of the proposed method.

AdaBoost is a classic ensemble learning algorithm with good classifier performance. In the past, it mainly used weak classifier as base classifier, such as KNN. They are simple and easy to train, but the essence of the weak classifier, it is impossible to get very high classification accuracy. In order to improve the correct rate, this paper introduces the AdaBoost ensemble classifier based on convolutional neural network, namely adaBoost-CNN, referred to as ACNN. ACNN design a new training method, it not only gives the weight of base classifier according to the error rate of base classifier in pre-training phase, but also dynamically adjusts this weight and learning coefficient of training sample according to the error rate of each class in ensemble training phase. Finally, through experiments on some public datasets, it was proved that ACNN not only can effectively reduce the classification error rate, but also can solve the problem of class recognition rate imbalanced caused by similar categories or training samples quantity deviation.

Although DOTA2 is a popular game around the world, no clear algorithm or software are designed to forecast the winning probability by analyzing the lineups. However, the author finds that Naive Bayes classifier, one of the most common classification algorithm, can analyze the lineups and predict the outcome according to the lineups and gives an improved Naive Bayes classifier. Using the DOTA2 data set published in the UCI Machine Learning Repository, we test Naive Bayes classifier's prediction of respective winning probability of both sides in the game. The results show that Naive Bayes classifier is a practical tool to analyze the lineups and predict the outcome based on players' choices.

This study examined the electrocardiographic data set recorded by Boston's Beth Israel Hospital for the work of the cardiac and neurotransmitter system and for normal and various irregular heartbeat patterns of electrical activity in the heart. Seven different types of arrhythmia, available in this data set, were classified using four different, widely used, classifiers (Fuzzy C-Means, Naive Bayes, Extreme Learning Machine and K-Means) by multiple classification methods. Classifier performances were evaluated using accuracy, sensitivity, and selectivity classification performance measures. The results of the study showed that classification achievements for four classifiers had the highest success rate of 99% of "Normal" beat type compared to other types of arrhythmia. The average classification performances of Naive Bayes and Extreme Learning Machine classifiers were found to be higher when the classifiers were compared among themselves. When the averages of all the arrhythmia types were taken the most successful classifier was detected as Naive Bayes classifier with 92% accuracy and 95% selectivity values.

Mass spectrometry is one of the widely utilized important methods to study protein functions and components. The challenge of mono-isotope pattern recognition from large scale protein mass spectral data needs computational algorithms and tools to speed up the analysis and improve the analytic results. We utilized naïve Bayes network as the classifier with the assumption that the selected features are independent to predict mono-isotope pattern from mass spectrometry. Mono-isotopes detected from validated theoretical spectra were used as prior information in the Bayes method. Three main features extracted from the dataset were employed as independent variables in our model. The application of the proposed algorithm to publicMo dataset demonstrates that our naïve Bayes classifier is advantageous over existing methods in both accuracy and sensitivity.

In view of the higher mistaken-detection rate problem of the human face detection in complex conditions, we put forward an improved algorithm. This article Proposes one kind of the method Which unifies the level of difference between the threshold and the feature value of the weak classifier with the weak classifier's overall error rate. Compared to the method which only based on the overall classification error rate to update the weights, this method can achieve higher detection rate while reduces the mistaken-detection rate. This article redefines the training error which is caused When we train the weak classifier, and Proposes MCE-AdaBoost algorithm. The new definition of training error will pay more attention to the error Which erroneously estimates the face Sample as non-face sample; this much more conforms to face detection of this special target detection issue. The experimental results show that MCE-AdaBoost algorithm can effectively improve the detection Performance of the final classifier.

In this brief, we show that minimizing nearest neighbor classification error (MNNE) is a favorable criterion for supervised linear dimension reduction (SLDR). We prove that MNNE is better than maximizing mutual information in the sense of being a proxy of the Bayes optimal criterion. Based on kernel density estimation, we derive a nonparametric algorithm for MNNE. Experiments on benchmark data sets show the superiority of MNNE over existing nonparametric SLDR methods.

an improved algorithm of Bayesian classifier combined with Fisher Linear Discriminant Analysis is proposed. This algorithm is the key to search the projection space of maximum separation. The original samples are projected to maximum separation space and new samples are obtained. These new samples are classifed by Bayes classifier. Experimental results show that improved Bayesian classifier has higher accuracy of classification and better performance of classification in the given data collection.

Naive Bayes (NB) is one of the top 10 algorithms thanks to its simplicity, efficiency, and interpretability. To weaken its attribute independence assumption, naive Bayes tree (NBTree) has been proposed. NBTree is a hybrid algorithm, which deploys a naive Bayes classifier on each leaf node of the built decision tree and has demonstrated remarkable classification performance. When comes to text classification tasks, multinomial naive Bayes (MNB) has been a dominant modeling approach after the multi-variate Bernoulli model. Inspired by the success of NBTree, we propose a new algorithm called multinomial naive Bayes tree (MNBTree) by deploying a multinomial naive Bayes text classifier on each leaf node of the built decision tree. Different from NBTree, MNBTree builds a binary tree, in which the split attributes' values are just divided into zero and nonzero. At the same time, MNBTree uses the information gain measure instead of the classification accuracy measure to build the tree for reducing the time consumption. To further scale up the classification performance of MNBTree, we propose its multiclass learning version called multiclass multinomial naive Bayes tree (MMNBTree) by applying the multiclass technique to MNBTree. The experimental results on a large number of widely used text classification benchmark datasets validate the effectiveness of our proposed algorithms: MNBTree and MMNBTree.

High accuracy and low overhead are two key features of a well-designed classifier for different classification scenarios. In this paper, we propose an improved classifier using a single-hidden layer feedforward neural network (SLFN) trained with extreme learning machine. The novel classifier first utilizes principal component analysis to reduce the feature dimension and then selects the optimal architecture of the SLFN based on a new localized generalization error model in the principal component space. Experimental and statistical results on the NSL-KDD data set demonstrate that the proposed classifier can achieve a significant performance improvement compared with previous classifiers.