Meta-heuristic Feature Selection Research Articles

Best Features Selection for Biomedical Data Classification Using Seven Spot Ladybird Optimization Algorithm

This article presents a new adaptive algorithm called FS-SLOA (Feature Selection-Seven Spot Ladybird Optimization Algorithm) which is a meta-heuristic feature selection method based on the foraging behavior of a seven spot ladybird. The new efficient technique has been applied to find the best subset features, which achieves the highest accuracy in classification using three classifiers: the Naive Bayes (NB), the Nearest Neighbors (KNN) and the Support Vector Machine (SVM). The authors' proposed approach has been experimented on four well-known benchmark datasets (Wisconsin Breast cancer, Pima Diabetes, Mammographic Mass, and Dermatology datasets) taken from the UCI machine learning repository. Experimental results prove that the classification accuracy of FS-SLOA is the best performing for different datasets.

Using Penguins Search Optimization Algorithm for Best Features Selection for Biomedical Data Classification

Feature selection is essential to improve the classification effectiveness. This paper presents a new adaptive algorithm called FS-PeSOA (feature selection penguins search optimization algorithm) which is a meta-heuristic feature selection method based on “Penguins Search Optimization Algorithm” (PeSOA), it will be combined with different classifiers to find the best subset features, which achieve the highest accuracy in classification. In order to explore the feature subset candidates, the bio-inspired approach PeSOA generates during the process a trial feature subset and estimates its fitness value by using three classifiers for each case: Naive Bayes (NB), Nearest Neighbors (KNN) and Support Vector Machines (SVMs). Our proposed approach has been experimented on six well known benchmark datasets (Wisconsin Breast Cancer, Pima Diabetes, Mammographic Mass, Dermatology, Colon Tumor and Prostate Cancer data sets). Experimental results prove that the classification accuracy of FS-PeSOA is the highest and very powerful for different datasets.

Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images

This paper presents an automatic mitosis detection approach of histopathology slide imaging based on using neutrosophic sets (NS) and moth-flame optimization (MFO). The proposed approach consists of two main phases, namely candidate’s extraction and candidate’s classification phase. At candidate’s extraction phase, Gaussian filter was applied to the histopathological slide image and the enhanced image was mapped into the NS domain. Then, morphological operations have been implemented to the truth subset image for more enhancements and focus on mitosis cells. At candidate’s classification phase, several features based on statistical, shape, texture and energy features were extracted from each candidate. Then, a principle of the meta-heuristic MFO algorithm was adopted to select the best discriminating features of mitosis cells. Finally, the selected features were used to feed the classification and regression tree (CART). A benchmark dataset consists of 50 histopathological images was adopted to evaluate the performance of the proposed approach. The adopted dataset consists of five distinct breast pathology slides. These slides were stained with H&E acquired by Aperio XT scanners with 40-x magnification. The total number of mitoses in 50 database images is 300, which were annotated by an expert pathologist. Experimental results reveal the capability of the MFO feature selection algorithm for finding the optimal feature subset which maximizing the classification performance compared to well-known and other meta-heuristic feature selection algorithms. Also, the high obtained value of accuracy, recall, precision and f-score for the adopted dataset prove the robustness of the proposed mitosis detection and classification approach. It achieved overall 65.42 % f-score, 66.03 % recall, 65.73 % precision and accuracy 92.99 %. The experimental results show that the proposed approach is fast, robust, efficient and coherent. Moreover, it could be used for further early diagnostic suspicion of breast cancer.

Face Recognition based on a Hybrid Meta-heuristic Feature Selection Algorithm

For the past few years, a number of new face recognition techniques have been proposed. Always it is a big unanswered question among face recognition researchers about which method or technique will have better performance. In this study an approach to recognize known faces based on Eigen vectors and a hybrid Meta-heuristic feature selection algorithm is proposed. The eigenvectors which are covariance matrix of the face images together describes the difference between face images. Face recognition problem is viewed as a two dimensional recognition problem. Initially the face images are projected in to face space and using Principal component analysis the eigenvectors with high Eigenvalues are extracted to reduce the dimension of the feature vector. Further to select the best feature vectors which increase the classification accuracy is selected by using a hybrid metaheuristic algorithm using Genetic algorithm (GA) and Bacteria Foraging Optimization (BFO). In this study the Support vector machine (SVM) and Back propagation neural network (BPNN) are used for classification. The classifiers are trained and tested separately using the frontal face images taken from AT&T database. The SVM and BPNN produces an average classification accuracy of 82.6% and 83.28% respectively.

Creating diverse nearest-neighbour ensembles using simultaneous metaheuristic feature selection

The nearest-neighbour (1NN) classifier has long been used in pattern recognition, exploratory data analysis, and data mining problems. A vital consideration in obtaining good results with this technique is the choice of distance function, and correspondingly which features to consider when computing distances between samples. In recent years there has been an increasing interest in creating ensembles of classifiers in order to improve classification accuracy. This paper proposes a new ensemble technique which combines multiple 1NN classifiers, each using a different distance function, and potentially a different set of features (feature vector). These feature vectors are determined for each distance metric simultaneously using Tabu Search to minimise the ensemble error rate. We show that this approach implicitly selects for a diverse set of classifiers, and by doing so achieves greater performance improvements than can be achieved by treating the classifiers independently, or using a single feature set. Naturally, optimising the level of ensembles necessitates a much larger solution space, to make this approach tractable, we show how Tabu Search at the ensemble level can be hybridised with local search at the level of individual classifiers. The proposed ensemble classifier with different distance metrics and different feature vectors is evaluated using various benchmark datasets from UCI Machine Learning Repository and a real-world machine-vision application. Results have indicated a significant increase in the performance when compared with various well-known classifiers. Furthermore, the proposed ensemble method is also compared with ensemble classifier using different distance metrics but with same feature vector (with or without feature selection (FS)).