A Survey of Feature Selection Strategies for DNA Microarray Classification

Classification tasks are difficult and challenging in the bioinformatics field, that used to predict or diagnose patients at an early stage of disease by utilizing DNA microarray technology. However, crucial characteristics of DNA microarray technology are a large number of features and small sample sizes, which means the technology confronts a "dimensional curse" in its classification tasks because of the high computational execution needed and the discovery of biomarkers difficult. To reduce the dimensionality of features to find the significant features that can employ feature selection algorithms and not affect the performance of classification tasks. Feature selection helps decrease computational time by removing irrelevant and redundant features from the data. The study aims to briefly survey popular feature selection methods for classifying DNA microarray technology, such as filters, wrappers, embedded, and hybrid approaches. Furthermore, this study describes the steps of the feature selection process used to accomplish classification tasks and their relationships to other components such as datasets, cross-validation, and classifier algorithms. In the case study, we chose four different methods of feature selection on two-DNA microarray datasets to evaluate and discuss their performances, namely classification accuracy, stability, and the subset size of selected features. Keywords: Brief survey; DNA microarray data; feature selection; filter methods; wrapper methods; embedded methods; and hybrid methods. DOI: 10.7176/CEIS/14-2-01 Publication date:March 31st 202

An Improved Crow Search Algorithm with Grey Wolf Optimizer for High-Dimensional Optimization Problems

© 2022, Springer Nature Switzerland AG.Crow search algorithm (CSA) mainly solves optimization problems. In high-dimensional optimization problems, CSA searches with moves toward the wrong crows’ hiding position. Solving the problems of the CSA algorithm, this paper proposes an improved CSA with Grey Wolf Optimization (GWO) algorithms is called ICSAGWO for manipulating the high-dimensional optimization problem. The main idea is to hybrid both algorithms’ strengths that utilize the efficient exploitation ability of CSA with good performance in the exploration ability and convergence speed of GWO. By hybridizing, the authors employ an adaptive inertia weight to control exploitation and exploration capacities. ICSAGWO algorithm is tested on twenty-three benchmark functions with 30 to 500 dimensions and compared among other algorithms, such as GSA, WOA, GWO, CSA, etc. Experimental results of the proposed algorithm ICSAGWO obtain high performance in both unimodal and multimodal and not affecting the search performance even in high dimension data over other algorithms