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Discovering a hearing disorder at an earlier intervention is critical for reducing the effects of hearing loss and the approaches to increase the remaining hearing ability can be implemented to achieve the successful development of human communication. Recently, the explosive dataset features have increased the complexity for audiologists to decide the proper treatment for the patient. In most cases, data with irrelevant features and improper classifier parameters causes a crucial influence on the audiometry system in terms of accuracy. This is due to the dependent processes of these two, where the classification accuracy performance could be worsened if both processes are conducted independently. Although the filter algorithm is capable of eliminating irrelevant features, it still lacks the ability to consider feature reliance and results in a poor selection of significant features. Improper kernel parameter settings may also contribute to poor accuracy performance. In this paper, an ensemble filters feature selection based on Information Gain (IG), Gain Ratio (GR), Chi-squared (CS), and Relief-F (RF) with harmonize optimization of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is presented to mitigate these problems. Ensemble filters are utilized so that the initial top dominant features relevant for classification can be considered. Then, PSO and SVM are optimized simultaneously to achieve the optimal solution. The results on a standard Audiology dataset show that the proposed method produces 96.50% accuracy with optimal solution compared to classical SVM, which signifies the proposed method is effective in handling high dimensional data for hearing disorder prediction. [ABSTRACT FROM AUTHOR]

Biological data generally contain complex and high-dimensional samples. In addition, the number of samples in biological datasets is much fewer than the number of features, so the vast number of features should be selected carefully and determine the optimal subset of features. Feature selection (FS) is a vital stage in biological data mining applications (e.g., classification) for dealing with the curse of dimensionality problems and finding highly informative features. This work proposes an effective FS approach based on a new version of Gray Wolf Optimizer (GWO) called Multi-strategy Gray Wolf Optimizer (MSGWO) for better features selection for biological data classification. The use of MSGWO in feature selection is to find the optimal subset of features between classes, solve premature convergence, and enhance the local search ability of the GWO algorithm. Multiple exploration and exploitation strategies are proposed to enhance the global search and local search abilities of the GWO algorithm through the optimization process. The support vector machine (SVM) classifier is used to evaluate the proposed GWO-based FS approaches. MSGWO was evaluated on thirteen high-dimensional biological datasets obtained from the UCI repository with a smaller number of instances. The reported results confirm that employing multiple exploration and multiple exploitation strategies is highly useful for enhancing the search tendency of the MSGWO in the FS domain. Statistical tests proved that the superiority of the proposed approach is statistically significant as compared to the basic GWO and similar wrapper-based FS techniques, including binary particle swarm optimization (BPSO), binary bat algorithm (BBA), binary gravitational search algorithm (BGSA), and binary whale optimization algorithm (BWOA). In terms of classification accuracy, MSGWO yielded better accuracy rates than the standard GWO algorithm on 84% of applied biological datasets. MSGWO also recorded better accuracy rates than its other competitors in all 13 cases. In terms of the lowest number of selected features, MSGWO yielded excellent reduction rates compared to its peers. [ABSTRACT FROM AUTHOR]

The experts' decisions and evaluating the patients' data are the most significant parts affecting the breast cancer analysis. For early breast cancer detection, numerous techniques of machine learning not only can assist in examining and diagnosis the medical data quickly but also decrease the potential errors that could be occurred due to inexpert or unskilled decision-makers. Support vector machine is one of the famous classifiers that has already made an important contribution to the field of cancer classification. However, configurations of different kernel function and their parameters can significantly affect the performance of the SVM classifier. To further improve the classification accuracy of the SVM classifier for breast cancer diagnosis, an intelligent cancer classification method is proposed based on selecting a feature subset and optimizing the relevant parameters (i.e., penalty factor parameter (c ) and kernel parameter γ ) of the SVM classifier concurrently through an intelligent algorithm using the Jaya algorithm. Then, this method (Jaya-SVM) was applied to precisely characterize the breast cancer dataset, including 699 samples, which are 458 and 241 for benign and malignant, respectively. Furthermore, to evaluate the effectiveness of the proposed Jaya-SVM classifier, it is compared in terms of the computational complexity and the classification accuracy with several combinatorial metaheuristic classifiers, namely the genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and cuckoo search (CS) based-SVM. Apart from this, a Breast Cancer Coimbra Dataset taken from the UCI library is used to validate the effectiveness of the proposed method. The results are presented, explained, and conclusions are drawn. [ABSTRACT FROM AUTHOR]

In medical science, collecting and classifying data from various diseases is a vital task. The confused and large amounts of data are problems that prevent us from achieving acceptable results. One of the major problems for diabetic patients is a failure to properly diagnose the disease. As a result of this mistake in diagnosis or failure in early diagnosis, the patient may suffer from complications such as blindness, kidney failure, and cutting off the toes. Nowadays, doctors diagnose the disease by relying on their experience and knowledge and performing complex and time-consuming tests. One of the problems with current diabetic, diagnostic methods is the lack of appropriate features to diagnose the disease and consequently the weakness in its diagnosis, especially in its early stages. Since diabetes diagnosis relies on large amounts of data with many parameters, it is necessary to use machine learning methods such as support vector machine (SVM) to predict the complications of diabetes. One of the disadvantages of SVM is its parameter adjustment, which can be accomplished using metaheuristic algorithms such as particle swarm optimization algorithm (PSO), genetic algorithm, or grey wolf optimizer (GWO). In this paper, after preprocessing and preparing the dataset for data mining, we use SVM to predict complications of diabetes based on selected parameters of a patient acquired by laboratory test using improved GWO. We improve the selection process of GWO by employing dynamic adaptive middle filter, a nonlinear filter that assigns appropriate weight to each value based on the data value. Comparison of the final results of the proposed algorithm with classification methods such as a multilayer perceptron neural network, decision tree, simple Bayes, and temporal fuzzy min–max neural network (TFMM-PSO) shows the superiority of the proposed method over the comparable ones. [ABSTRACT FROM AUTHOR]

A Gaussian based Particle Swarm Optimization Gravitational Search Algorithm (GPSOGSA) is being proposed for extensive feature selection that serves highly in making effective predictions. GPSOGSA helps to overcome the problem of being stuck into the local optima and influences the local searching ability, thus it aims to bridge the gap of exploration and exploitation. The algorithm also limits the usage of too many parameters like acceleration factors, maximum velocity, inertia weight that plays a vital role in PSO, GSA and PSOGSA. The efficacy of the algorithm has been tested upon unimodal and multimodal benchmark functions. We have also evaluated the performance of the algorithm by applying it on various benchmark datasets. The algorithm uses a wrapper-based approach that includes Support Vector Machine as a learner algorithm, and improves both the execution time and the performance accuracy. The findings show that the proposed algorithm could escape from local optimum and converges faster than the PSO, GSA and PSOGSA algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a content-based image retrieval technique that focuses on extraction and reduction in multiple features. To obtain multi-level decomposition of the image by extracting approximation and correct coefficients, discrete wavelet transformation is applied to the RGB channels initially. Therefore, both approximation and correct coefficients are applied to the dominant rotated local binary pattern termed as texture descriptor which is computationally effective and rotationally invariant. For a local neighbor patch, a rotation invariance function image is obtained by measuring the descriptor relative to the reference. The proposed approach contains the complete structural information extracted from the local binary patterns and also extracts the additional information using the information of magnitude, thereby achieving extra discriminative power. Then, GLCM description is used by obtaining the dominant rotated local binary pattern image to extract the statistical characteristics for texture image classification. The proposed technique is applied to CORAL dataset with the help of particle swarm optimization-based feature selector to minimize the number of features that can be used during the classification process. The three classifiers, i.e., support vector machine, K-nearest neighbor, and decision tree, are trained and tested. The comparison is based in terms of Accuracy, precision, recall, and F-measure performance metrics for classification. Experimental results show that the proposed approach achieves better accuracy, precision, recall, and F-measure values for most of the CORAL dataset classes. [ABSTRACT FROM AUTHOR]

The intrusion detection is an essential section in network security because of its immense volume of threats which bothers the computing systems. The real-time intrusion detection dataset comprises redundant or irrelevant features. The duplicate features make it quite challenging to locate the patterns for intrusion detection. Hybrid optimization scheme (HOS) is designed for combining adaptive artificial bee colony (AABC) with adaptive particle swarm optimization (APSO) for detecting intrusive activities. The schemes are aggregated for locating improved optimization-based outcomes, and the precision during categorization is acquired using tenfold cross-validation scheme. The main objective of the proposed method is to improve the rate of precision in intrusion activities in internetwork by choosing the relevant features. Effectiveness of the hybrid categorization scheme is accessed using an NSL-KDD dataset. Single feature selection method and random feature selection method are used to assess the proposed HOS intrusion detection approaches. The effectiveness of the designed scheme is evaluated with existing machine learning schemes such as Naive Bayes, AABC, APSO, and support vector machine, which outperform the HOS. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is a well-regarded machine learning algorithm widely applied to classification tasks and regression problems. SVM was founded based on the statistical learning theory and structural risk minimization. Despite the high prediction rate of this technique in a wide range of real applications, the efficiency of SVM and its classification accuracy highly depends on the parameter setting as well as the subset feature selection. This work proposes a robust approach based on a recent nature-inspired metaheuristic called multi-verse optimizer (MVO) for selecting optimal features and optimizing the parameters of SVM simultaneously. In fact, the MVO algorithm is employed as a tuner to manipulate the main parameters of SVM and find the optimal set of features for this classifier. The proposed approach is implemented and tested on two different system architectures. MVO is benchmarked and compared with four classic and recent metaheuristic algorithms using ten binary and multi-class labeled datasets. Experimental results demonstrate that MVO can effectively reduce the number of features while maintaining a high prediction accuracy. [ABSTRACT FROM AUTHOR]