Improving Feature Subset Selection Using a Genetic Algorithm for Microarray Gene Expression Data

Abstract

Microarray data usually contains a huge number of genes (features) and a comparatively small number of samples, which make accurate classification or prediction of diseases challenging. Feature selection techniques can help us identify important and irrelevant (unimportant) features by applying certain selection criteria. However, different feature selection algorithms based on various theoretical arguments often produce different results when applied to the same data set. This makes selecting an optimal or near optimal feature subset for a data set difficult. In this paper, we propose using a genetic algorithm to improve feature subset selection by combining valuable outcomes from multiple feature selection methods. The goal of our genetic algorithm is to achieve a balance between the classification accuracy and the size of the feature subsets selected. The advantages of this approach include the ability to accommodate different feature selection criteria and find small subsets of features that perform well for a particular inductive learning algorithm of interest to build the classifier. The experimental results demonstrate that our approach can find subsets of features with higher classification accuracy and/or smaller size compared with each individual feature selection algorithm.

A framework model using multifilter feature selection to enhance colon cancer classification

“…This was followed by a hierarchical grouping of the genes selected, after which an analysis was carried out using the classification algorithms C4.5, KNN, NB and SVM. The hybrid approach used by Tan et al [ 61 ] involved a feature selection enhancement of a sample of genes using a GA; this was achieved by combining the best results from a group of feature selection methods, after which SVM were used to analyse the data. Kim and Cho [ 62 ] classified genes by employing an evolutionary neural network, while Mohamad et al [ 19 ] made their selection of genes from microarray data, using a Cyclic-GASVM hybrid method.…”

Selección de Características de Microarreglos de ADN Utilizando una Búsqueda Cuckoo

“…MYOSIN HEAVY CHAIN, NONMUSCLE (Gallus gallus) [55], [56], [52], [57], [58], [59], [60], [38], [61] M76378 Human cysteine-rich protein (CRP) gene, exons 5 and 6.…”

“…[60], [47], [66], [52], [49], [67], [68], [69] H43887 COMPLEMENT FACTOR D PRECURSOR (Homo sapiens) [56], [57], [58], [59] Un total de cinco genes relevantes han sido seleccionados de la base de cáncer de pulmón. La selección de estos genes se debe a que el clasificador logro separar correctamente la información contenida en la base de datos, esto significa que el clasificador ha logrado separar la clase Malignant Pleural Mesothelioma (MPM) de la clase Adenocarcinoma (ADCA).…”

Hybrid Feature Selection Using Genetic Algorithm and Information Theory

“…In order to estimate the performance of the proposed method, we applied our method on University of California-Irvine (UCI) machine-learning data sets [14]. Experimental results showed that our method is effective and efficient in finding small subsets of the significant features for reliable classification.…”

“…Evaluate the fitness values of all individuals in the population using a classifier to evaluate each chromosome (the selected feature subset) based on the classification accuracy and the dimension of the feature subset. Tan et al [14] proposed the following fitness function in order to optimize two objectives: maximize the classification accuracy of the feature subset and minimize the size of the feature subset.…”