Different approaches have been proposed for Feature selection to obtain suitable Features subset among all Features. These methods search Feature space for Feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods. In filter methods, Features subsets are selected due to some measures like inter-class distance, Features statistical independence or information theoretic measures. Even though, wrapper methods use a classifier to evaluate Features subsets by their predictive accuracy (on test data) by statistical resampling or cross-validation. Filter methods usually use only one measure for Feature selection that does not necessarily produce the best result. In this paper, we proposed to use the classification error measures besides to filter measures where our classifier is support vector machine (SVM). To this end, we use multi objective genetic algorithm. In this way, one of our Feature selection measure is SVM classification error. Another measure is selected between mutual information and Laplacian criteria which indicates informative content and structure preserving property of Features, respectively. The evaluation results on the UCI datasets show the efficiency of this method.

Unsupervised methods have become essential for managing and analyzing large datasets due to their ability to uncover underlying patterns without labeled data. In particular, unsupervised learning techniques have shown promise in enhancing the robustness of Feature vectors, which can subsequently improve the accuracy and efficiency of classification models. This study presents a novel approach called Automatic Weighted Clustering (AWC), designed as an unsupervised algorithm specifically aimed at optimizing data segmentation to support and enhance the classification performance of supervised algorithms. The AWC algorithm, applied in the context of medical datasets, enables efficient knowledge discovery by automatically identifying and weighting important data Features. To assess AWC’s performance, we conducted extensive experiments across various datasets to test the algorithm’s generalizability and scalability. The AWC approach yielded nine distinct clusters from the dataset, demonstrating a 6.26% accuracy improvement compared to the standard Feature vector and a 4.83% increase over the traditional K-means clustering method. Additionally, AWC achieved a 30.68% reduction in execution time, highlighting its potential for faster, more accurate medical data analysis and classification.

Breast cancer is known to be among the most prevalent cause of mortality among women. Since early breast cancer diagnosis increases survival chances, the development of a system with a highly accurate output to detect suspicious masses in mammographic images is of great significance. Thus, many studies have focused on the development of methods with favorable performance and acceptable accuracy to detect cancerous masses, proposed various techniques to diagnose breast cancer, and compared their accuracies. Most previous studies have used composite selection and Feature reduction techniques to detect breast cancer and accelerate its treatment; however, most have failed to reach the desired accuracy due to the selection of ineffective Features and the lack of a proper analytical method for the Features. The present study reviews the methods proposed to detect breast cancer so far and analyzes the process of Feature vector optimization techniques as well as the normal/abnormal and benign/malignant mass classification.

Recently, a large set of electroencephalography (EEG) data is being generated by several high-quality labs worldwide and is free to be used by all researchers in the world. On the other hand, many neuroscience researchers need these data to study different neural disorders for better diagnosis and evaluating the treatment. However, some format adaptation and pre-processing are necessary before using these available data. In this paper, we introduce the SecondBrain as a new lightweight and simplified module that can easily apply various major analysis on EEG data with common data formats. The characteristics of the SecondBrain shows that it is suitable for everyday usage with medium analyzing power. It is easy to learn and accept many data formats. The SecondBrain module has been developed with Python and has the power to windowing data, whitening transform, independent component analysis (ICA), downloading the public datasets, computing common spatial patterns (CSP) and other useful analysis. The SecondBrain, also, employs a common spatial pattern (CSP) to extract Features and classifying the EEG MI-based data through support vector machine (SVM). We achieved a satisfactory result in terms of speed and performance.