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In this article, a new kind of 2D tool is proposed, namely ⟨φ, δ ⟩ diagrams, able to highlight most of the information deemed relevant for classifier building and assessment. In particular, accuracy, bias and break-even points are immediately evident therein. These diagrams come in two different forms: the first is aimed at representing the phenomenon under investigation in a space where the imbalance between negative and positive samples is not taken into account, the second (which is a generalization of the first) is able to visualize relevant information in a space that accounts also for the imbalance. According to a specific design choice, all properties found in the first space hold also in the second. The combined use of φ and δ can give important information to researchers involved in the activity of building intelligent systems, in particular for classifier performance assessment and feature ranking/selection. [ABSTRACT FROM AUTHOR]

In this paper, a new type of multilayer rule-based classifier is proposed and applied to image classification problems. The proposed approach is entirely data-driven and fully automatic. It is generic and can be applied to various classification and prediction problems, but in this paper we focus on image processing, in particular. The core of the classifier is a fully interpretable, understandable, self-organized set of IF…THEN… fuzzy rules based on the prototypes autonomously identified by using a one-pass type training process. The classifier can self-evolve and be updated continuously without a full retraining. Due to the prototype-based nature, it is non-parametric; its training process is non-iterative, highly parallelizable and computationally efficient. At the same time, the proposed approach is able to achieve very high classification accuracy on various benchmark datasets surpassing most of the published methods, be comparable with the human abilities. In addition, it can start classification from the first image of each class in the same way as humans do, which makes the proposed classifier suitable for real-time applications. Numerical examples of benchmark image processing demonstrate the merits of the proposed approach. [ABSTRACT FROM AUTHOR]

Online learning involves extracting information from large quantities of data (streams) usually affected by changes in the distribution (concept drift). A drift detector is a small program that estimates the positions of these changes to replace the base learner and ultimately improve overall accuracy. This article reports on a large-scale comparison of 14 concept drift detector configurations for mining fully labeled data streams with concept drift, using a large number of artificial datasets and two different base classifiers (Naive Bayes and Hoeffding Tree). The goal is to adequately measure how good the existent concept drift detectors really are and also to verify and challenge a common belief in the area, namely that the best drift detection methods are necessarily those that detect all the existing drifts closer to their correct positions, and only them, irrespective of the fact that different objectives usually require alternative solutions. Finally, to some extent, this article may also be seen as an extensive literature survey of concept drift detectors. [ABSTRACT FROM AUTHOR]

Multitarget regression has recently generated intensive popularity due to its ability to simultaneously solve multiple regression tasks with improved performance, while great challenges stem from jointly exploring inter-target correlations and input–output relationships. In this paper, we propose multitarget sparse latent regression (MSLR) to simultaneously model intrinsic intertarget correlations and complex nonlinear input–output relationships in one single framework. By deploying a structure matrix, the MSLR accomplishes a latent variable model which is able to explicitly encode intertarget correlations via \ell 2,1 -norm-based sparse learning; the MSLR naturally admits a representer theorem for kernel extension, which enables it to flexibly handle highly complex nonlinear input-output relationships; the MSLR can be solved efficiently by an alternating optimization algorithm with guaranteed convergence, which ensures efficient multitarget regression. Extensive experimental evaluation on both synthetic data and six greatly diverse real-world data sets shows that the proposed MSLR consistently outperforms the state-of-the-art algorithms, which demonstrates its great effectiveness for multivariate prediction. [ABSTRACT FROM PUBLISHER]

Emotion-related feature extraction is a challenging task in speech emotion recognition. Due to the lack of discriminative acoustic features, classical approaches based on traditional acoustic features could not provide satisfactory performances. This research proposes a novel type of feature related to prominence, which, together with traditional acoustic features, are used to classify seven typical different emotional states. To this end, the author group produces a Chinese Dual-mode Emotional Speech Database (CDESD), which contains additional prominence and paralinguistic annotation information. Then, a consistency assessment algorithm is presented to validate the reliability of the annotation information of this database. The results show that the annotation consistency on prominence reaches more than 60% on average. Subsequently, this research analyzes the correlation of the prominence features with emotional states using a curve fitting method. Prominence is found to be closely related to emotion states, to retain emotional information at the word level to the greatest possible extent and to play an important role in emotional expression. Finally, the proposed prominence features are validated on CDESD through speaker-dependent and speaker-independent experiments with four commonly used classifiers. The results show that the average recognition rate achieved using the combined features is improved by 6% in speaker-dependent experiments and by 6.2% in speaker-independent experiments compared with that achieved using only acoustic features. [ABSTRACT FROM AUTHOR]

Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations. Relatively recently, declarative rules called matching dependencies (MDs) have been proposed for specifying similarity conditions under which attribute values in database records are merged. In this work we show the process and the benefits of integrating four components of ER: (a) Building a classifier for duplicate/non-duplicate record pairs built using machine learning (ML) techniques; (b) Use of MDs for supporting the blocking phase of ML; (c) Record merging on the basis of the classifier results; and (d) The use of the declarative language LogiQL —an extended form of Datalog supported by the LogicBlox platform—for all activities related to data processing, and the specification and enforcement of MDs. [ABSTRACT FROM AUTHOR]

Abstract: In this study, we propose a novel approach for designing fuzzy classifiers. The first part of our approach is a new preprocess algorithm called SPP (silhouette cluster validity index aided pre-process via k-means). The SPP algorithm has been performed on the data set to determine the numbers of the membership functions and their initial boundaries. Then, the Mopt-aiNetLS algorithm (modified version of opt-aiNet combined with local search strategy of memetic algorithm), the second part of the approach, examines search space to find the optimal values of fuzzy rules and membership functions for the system. The Mopt-aiNetLS is the combination of the memetic algorithm and a modified version of the opt-aiNet algorithm, in which some changes were made in the suppression and hypermutation mechanisms of the original opt-aiNet algorithm. These two new mechanisms are called the intelligent suppression mechanism and the adaptive hypermutation operator. Combining the modified version of opt-aiNet with the local search strategy of the memetic algorithm improves the accuracy of the classification rate. An effective search process has been realized using the Mopt-aiNetLS because the global search capability of opt-aiNet is complemented by the local search strategy of the memetic algorithm. To test the performance of this new approach, twenty different well-known classification dataset benchmark problems from the UCI dataset were used. The average 3×10 cross-fold validation results obtained from these datasets are presented and compared with the results of certain classification algorithms reported in the literature. The Wilcoxon Signed-Rank Test was also used for statistical comparisons. The obtained results demonstrate the effectiveness of the proposed approach. [Copyright &y& Elsevier]

Abstract: In this paper, a ν-twin support vector machine (ν-TSVM) is presented, improving upon the recently proposed twin support vector machine (TSVM). This ν-TSVM introduces a pair of parameters (ν) to control the bounds of the fractions of the support vectors and the error margins. The theoretical analysis shows that this ν-TSVM can be interpreted as a pair of minimum generalized Mahalanobis-norm problems on two reduced convex hulls (RCHs). Based on the well-known Gilbert’s algorithm, a geometric algorithm for TSVM (GA-TSVM) and its probabilistic speed-up version, named PGA-TSVM, are presented. Computational results on several synthetic as well as benchmark datasets demonstrate the significant advantages of the proposed algorithms in terms of both computation complexity and classification accuracy. [ABSTRACT FROM AUTHOR]

Supervised learning approaches to text classification axe in practice often required to work with small and unsystematically collected training sets. The alternative to supervised learning is usually viewed to he building classifier hand, using a domain expert's understanding of which features of tile text are related to the class of interest. This is expensive, requires a degree of sophistication about linguistics and classification, and makes it difficult to use combinations of weak predictors. We propose instead combining domain knowledge with training examples in a Bayesian framework. Domain knowledge is used to specify a prior distribution for the parameters of a logistic regression model, and labeled training data is used to produce a posterior distribution, whose mode we take as the final classifier. We show on three text categorization data sets that this approach can rescue what would otherwise be disastrously bad training situations, producing much more effective classifiers. [ABSTRACT FROM AUTHOR]

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The article presents the ordering of multiple adjectives and their categories including determiner, observation or opinion, and size and shape.

Question classification has become a crucial step in modern question answering systems. Previous work has demonstrated the effectiveness of statistical machine learning approaches to this problem. This paper presents a new approach to building a question classifier using log-linear models. Evidence from a rich and diverse set of syntactic and semantic features is evaluated, as well as approaches which exploit the hierarchical structure of the question classes. [ABSTRACT FROM AUTHOR]

The article offers information on the definition of terms related to revenue rulings and revenue procedures. Terms discussed include "Obsoleted," which describes a previously published ruling that is not considered determinative with respect to future transactions, "Revoked," which describes situations where the position in the previously published ruling is not correct, and "Modified," which describes that the substance of a previously published position is being changed.

The article presents information on the hieroglyphic representation of the word "Phallus" in Egypt. Phallus is written as "hnn" which presently known as khenen, in hieroglyph, this word was written as a twisted herb followed by two water signs and then a determinative sign. Though treated as having no value at all, determinative possessed a value of indicating a word on whether it will be a noun or a verb.