Feature Extraction and Selection via Robust Discriminant Analysis and Class Sparsity

In recent times, feature extraction was the focus of many researches due to its usefulness in the machine learning and pattern recognition fields. Feature extraction mainly aims to extract informative representations from the original set of features. This can be carried out using various ways. The proposed method is targeting a hybrid linear feature extraction scheme for supervised multi-class classification problems. Inspired by recent robust sparse LDA and Inter-class sparsity frameworks, we will propose a unifying criterion that is able to retain these two powerful linear discriminant method’s advantages. Thus, the obtained transformation encapsulates two different types of discrimination, the inter-class sparsity and robust Linear Discriminant Analysis with feature selection. We will introduce an iterative alternating minimization scheme in order to estimate the linear transform and the orthogonal matrix. The linear transform is efficiently updated via the steepest descent gradient technique. We will also introduce two initialization schemes for the linear transform. The proposed framework is generic in the sense that it allows the combination and tuning of other linear discriminant embedding methods. According to the experiments which have been carried out on several datasets including faces, objects and digits, the proposed method was able to outperform the competing methods in most cases.

Feature extraction methods have been successfully applied to many real-world applications. The classical Linear Discriminant Analysis (LDA) and its variants are widely used as feature extraction methods. Although they have been used for different classification tasks, these methods have some shortcomings. The main one is that the projection axes obtained are not informative about the relevance of original features. In this paper, we propose a linear embedding method that merges two interesting properties: Robust LDA and inter-class sparsity. Furthermore, the targeted projection transformation focuses on the most discriminant original features, The proposed method is called Robust Discriminant Analysis with Feature Selection and Inter-class Sparsity (RDA FSIS). Two kinds of sparsity are explicitly included in the proposed model. The first kind is obtained by imposing the `2;1 constraint on the projection matrix in order to perform feature ranking. The second kind is obtained by imposing the inter-class sparsity constraint used for getting a common sparsity structure in each class. Comprehensive experiments on five real-world image datasets demonstrate the effectiveness and advantages of our framework over existing linear methods.

A modified genetic algorithm and weighted principal component analysis based feature selection and extraction strategy in agriculture

Optimal Speech feature extraction which tries to acquire a parametric illustration of an input speech signal plays a vital role in the overall performance of an Automatic Speech Recognition (ASR) system. A good feature extraction technique along with feature selection algorithm should capture the important features of the signal and also should reject some irrelevant features. Feature selection is a critical task which can influence the performance of pattern classification and recognition system. In this paper, we have presented a hybrid evolutionary algorithm called Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) for optimal feature extraction and selection strategy. The proposed method consists of three stages, preprocessing, feature extraction and selection, and recognition. Initially, the preprocessing is done by wiener filter to reduce the noise level. Next, we extract eight type of statistical and acoustic features in the feature stage. The optimal set of extracted features is selected by using hybrid ABC-PSO algorithm. Finally, these optimized features are used for training the Support Vector Machine (SVM) classifier and based on these optimized features of the given input speech signal the corresponding text is displayed as the output. The proposed ASR is implemented on the MATLAB working platform and the experimental results show that overall performance of the system is high and the proposed hybrid algorithm is best suited for speech recognition.

The trend of current brain computer interfaces (BCI) seek to establish bi-directional communication with the brain, for instance, recovering motor functions by externally controlling devices and directly stimulating the brain. This will greatly assist paralyzed individuals through bypassing the damaged brain region. The key process of this communication interface is to decode movements from neural signals and encode information into neural activity. The majority of decoding or pattern classification studies have focused on cortical areas for BCIs, but deep brain structures have also been involved in motor control. The subthalamic nucleus (STN) in the basal ganglia is involved in the preparation, execution and imagining of movements, and may be an alternative source for driving BCIs. This study therefore aimed to classify patterns of deep brain local field potentials (LFPs) related to execution of visually cued movements. LFPs were recorded bilaterally from the STN through deep brain stimulation electrodes implanted in patients with Parkinson's disease. The frequency dependent components of the LFPs were extracted using the wavelet packet transform. In each frequency component, signal features were extracted using an alternative approach called neural synchronization by analyzing Granger causality between the STN. Based on these extracted features, a new feature selection strategy, namely weighted sequential feature selection (WSFS) was developed to efficiently select the optimal feature subset. A support vector machine (SVM) classifier was implemented alongside this novel feature extraction and selection strategy, and evaluated using a cross-validation procedure. Using this optimised feature subset, average correct pattern classification accuracy of movement (left or right) reached 76.0±3.1%. The results obtained in this study are encouraging and suggest that the neural activity in the deep neural circuit (basal ganglia) can be used for controlling BCIs.

To recognize expressions accurately, facial expression systems require robust feature extraction and feature selection methods. In this paper, a normalized mutual information based feature selection technique is proposed for FER systems. The technique is derived from an existing method, that is, the max-relevance and min-redundancy (mRMR) method. We, however, propose to normalize the mutual information used in this method so that the domination of the relevance or of the redundancy can be eliminated. For feature extraction, curvelet transform is used. After the feature extraction and selection the feature space is reduced by employing linear discriminant analysis (LDA). Finally, hidden Markov model (HMM) is used to recognize the expressions. The proposed FER system (CNF-FER) is validated using four publicly available standard datasets. For each dataset, 10-fold cross validation scheme is utilized. CNF-FER outperformed the existing well-known statistical and state-of-the-art methods by achieving a weighted average recognition rate of 99 % across all the datasets.

Classifying the gene expression levels of normal and cancerous cells and identifying the genes most contributing to this distinction propose an alternative means of diagnosis. We have investigated the effect of feature extraction and feature selection on clustering of the expression data on two different data sets for ovarian cancer. One data set consisted of 2176 transcripts from 30 samples, nine from normal ovarian epithelial cells and 21 from cancerous ones. The other data set had 7129 transcripts coming from 27 tumor and four normal ovarian tissues. Hierarchical clustering algorithms employing complete-link, average-link and Ward's method were implemented for comparative evaluation. Principal component analysis was applied for feature extraction and resulted in 100% segregation. Feature selection was performed to identify the most distinguishing genes using CART/spl reg/ software. Selected features were able to cluster the data with 100% success. The results suggest that adoption of feature extraction and selection enhances the quality of clustering of gene expression data for ovarian cancer. Identification of distinguishing genes is a more complex problem that requires incorporating pathway knowledge with statistical and machine learning methods.

Motor imagery (MI) from human brain signals can diagnose or aid specific physical activities for rehabilitation, recreation, device control, and technology assistance. It is a dynamic state in learning and practicing movement tracking when a person mentally imitates physical activity. Recently, it has been determined that a brain–computer interface (BCI) can support this kind of neurological rehabilitation or mental practice of action. In this context, MI data have been captured via non-invasive electroencephalogram (EEGs), and EEG-based BCIs are expected to become clinically and recreationally ground-breaking technology. However, determining a set of efficient and relevant features for the classification step was a challenge. In this paper, we specifically focus on feature extraction, feature selection, and classification strategies based on MI-EEG data. In an MI-based BCI domain, covariance metrics can play important roles in extracting discriminatory features from EEG datasets. To explore efficient and discriminatory features for the enhancement of MI classification, we introduced a median absolute deviation (MAD) strategy that calculates the average sample covariance matrices (SCMs) to select optimal accurate reference metrics in a tangent space mapping (TSM)-based MI-EEG. Furthermore, all data from SCM were projected using TSM according to the reference matrix that represents the featured vector. To increase performance, we reduced the dimensions and selected an optimum number of features using principal component analysis (PCA) along with an analysis of variance (ANOVA) that could classify MI tasks. Then, the selected features were used to develop linear discriminant analysis (LDA) training for classification. The benchmark datasets were considered for the evaluation and the results show that it provides better accuracy than more sophisticated methods.

Knowledge of the types of membrane protein provides useful clues in deducing the functions of uncharacterized membrane proteins. An automatic method for efficiently identifying uncharacterized proteins is thus highly desirable. In this work, we have developed a novel method for predicting membrane protein types by exploiting the discrimination capability of the difference in amino acid composition at the N and C terminus through split amino acid composition (SAAC). We also show that the ensemble classification can better exploit this discriminating capability of SAAC. In this study, membrane protein types are classified using three feature extraction and several classification strategies. An ensemble classifier Mem-EnsSAAC is then developed using the best feature extraction strategy. Pseudo amino acid (PseAA) composition, discrete wavelet analysis (DWT), SAAC, and a hybrid model are employed for feature extraction. The nearest neighbor, probabilistic neural network, support vector machine, random forest, and Adaboost are used as individual classifiers. The predicted results of the individual learners are combined using genetic algorithm to form an ensemble classifier, Mem-EnsSAAC yielding an accuracy of 92.4 and 92.2% for the Jackknife and independent dataset test, respectively. Performance measures such as MCC, sensitivity, specificity, F-measure, and Q-statistics show that SAAC-based prediction yields significantly higher performance compared to PseAA- and DWT-based systems, and is also the best reported so far. The proposed Mem-EnsSAAC is able to predict the membrane protein types with high accuracy and consequently, can be very helpful in drug discovery. It can be accessed at http://111.68.99.218/membrane.

Cardiovascular Disease (CVD) is one of the primary diseases that causes death every year. An approximation of roughly about 17.5 million people dies due to CVD, signifying about 31% of global deaths. Based on the statistics, every 34 seconds, a person dies due to heart disease. Various classification algorithms have been developed and utilized as classifiers to support doctors who are ineffectually diagnosed with heart disease. The main aim of this work is to improve the performance of the heart disease approach using image processing algorithm. To improve the effectiveness and efficiency of classification performance for heart disease diagnosis, an optimized neural network was proposed based on the feature extraction and selection approach for handling features. The objective of this investigation is to diagnose heart diseases using feature extraction, reduction based classification and image processing methods. The proposed model comprises of two subsets: Feature extraction using gray scale properties and Moth flame optimization (MFO) for effectual feature selection, followed by a classification technique using Generalized Regression Neural Network. The first system includes three stages: (i) Pre-processing of the dataset (ii) feature extraction (iii) performing MFO for efficient selection. In the second method, GRNN is proposed. The heart data set obtained from ACDC Challenge, was utilized for performing the computation. The image obtained from the MRI-scanner is in the NIfTI image format. The pre-process step used in this is to convert the image type from INT16 to uint8 to improve the quality of image viewing and for feature extraction process. In this phase, the texture properties from the pre-processed image are calculated and the value is in the numeric format. These values are the feature attributes of the dataset. The feature attributes of the image are given as input for the moth flame optimization process and output is the feature selected from the optimization process. The whole process is performed on the feature attributes of the image by determining the optimal feature for the classifier by reducing its error rate. The optimal feature from the moth flame optimization is used for training and testing the network. The classifier used in this approach is a single neural network classifier with a regression nature. Due to the regression property, the network is well trained with the feature. The Generalized regression neural network is used for classifying heart diseases. The proposed method achieves the accuracy of 96.23%, sensitivity of 95.41% and specificity of 96.75%. These values are calculated based on the confusion matrix of the classifier. In this, the feature extraction using the gray scale properties plays an important role to determine the feature attributes from the MRI heart images. The Moth flame optimization able to produce an accuracy of 97.23% using GRNN for classification with minimum single attribute mean of the image. It also outperformed the other methods, either the feature extraction based classification or the feature reduction based classification.

Feature selection using singular value decomposition and QR factorization with column pivoting for text-independent speaker identification

Feature Selection and Extraction Strategies in Data Mining

Breast cancer remains one of the principal reasons of death among women worldwide, highlighting the critical requirement for primary and exact diagnosis to expand patient persistence rates. Manual clarification of histopathological slides is frequently subjective, time-consuming, and prone to inter-observer unpredictability. Recent developments in machine learning (ML) and deep learning (DL) techniques have renovated histopathological image analysis by qualifying automated, consistent, and reproducible diagnostic systems. This review analysis thoroughly observes research from 2018 to 2025, covering advanced methods for breast cancer detection based on histopathological images, with feature extraction, feature selection, and classification strategies, and medical image investigations. In this study compares existing ML methods, convolutional neural networks (CNN), vision transformers (ViT), graph convolutional networks (GCN), ensemble learning and hybrid frameworks across numerous standard and multiple benchmark datasets. Performance comparisons encompass filter-based, wrapper-based, hybrid, and ensemble learning models. In this study highlights the benefits of AI-driven diagnostic systems such as enhanced accuracy, scalability, and consistency, while addressing key challenges including data discrepancy, inadequate interpretability, and high computational cost. This comparative review highlights publicly accessible datasets, standard architectures, and emerging techniques in explainable artificial intelligence, ViT, and federated learning. Finally, potential directions for future research are drawn to guide researchers, clinicians, and developers in the direction of proceeding intelligent systems for breast cancer histopathological diagnosis.

An epileptic seizure detector's performance definitely depends on features extraction and selection. In this study, we present the short-time average magnitude difference function (sAMDF) as a computationally efficient feature to distinguish seizures from EEG and it is compared with the frequently used curve length. We also suggest using a subspace based approach for feature selection that exploits divergence measure as the dissimilarity criterion. In this approach, basically features are linearly transformed into another reduced space for optimality while decreasing the computational burden. Seizure discrimination performances of transformed features and original features are compared. The obtained results demonstrate that the feature selection with a divergence-based subspace approach is quite useful to discriminate the seizure parts of the signal from the nonseizure ones

This study is aimed at assessing the usefulness of different feature selection and classification methodologies in the context of sleep apnea hypopnea syndrome (SAHS) detection. Feature extraction, selection and classification stages were applied to analyze blood oxygen saturation (SaO2) recordings in order to simplify polysomnography (PSG), the gold standard diagnostic methodology for SAHS. Statistical, spectral and nonlinear measures were computed to compose the initial feature set. Principal component analysis (PCA), forward stepwise feature selection (FSFS) and genetic algorithms (GAs) were applied to select feature subsets. Fisher's linear discriminant (FLD), logistic regression (LR) and support vector machines (SVMs) were applied in the classification stage. Optimum classification algorithms from each combination of these feature selection and classification approaches were prospectively validated on datasets from two independent sleep units. FSFS + LR achieved the highest diagnostic performance using a small feature subset (4 features), reaching 83.2% accuracy in the validation set and 88.7% accuracy in the test set. Similarly, GAs + SVM also achieved high generalization capability using a small number of input features (7 features), with 84.2% accuracy on the validation set and 84.5% accuracy in the test set. Our results suggest that reduced subsets of complementary features (25% to 50% of total features) and classifiers with high generalization ability could provide high-performance screening tools in the context of SAHS.