The Effect of Evolutionary Algorithm in Gene Subset Selection for Cancer Classification

Abstract

The fact that reflects the cancer research consequences shows that still there are improvements that should be investigated in the stream of cancer in future. This leads the researchers to actively involve further in cancer research field. As an invention, a hybrid machine learning method is proposed in this study where two filters are assessed along with a wrapper approach. Typically, filters prioritize the features while, wrappers contribute in subset identification. Though both filters and wrappers exist independently, the excellent results they produce when applied subsequently. The wrapperfilter combination plays a major role in feature selection. Yet, incorporating with a best strategy for feature space analysis is crucial in this concern. Thus, we introduce the Evolutionary Algorithm in the proposed study to search through the feature space for informative gene subset selection. Though there are several gene selection approaches for cancer classification, many of them suffer from law classification accuracy and huge gene subset for prediction. Hence, we propose Evolutionary Algorithm to overcome this problem. The proposed approach is evaluated on five microarray datasets, where three out of them provide 100% accuracy. Regardless the number of genes selected, both filters provide the same performance throughout the datasets used. As a consequence, the Evolutionary Algorithm in feature space search is highlighted for its performance in gene subset selection.

Movement Mode Harmony Search Based Multi-objective Firefly Algorithm Feature Selection for Detecting the Security Threats in Virtual Machine

“…The meta-heuristic algorithms and its improvement must have a perfect balance between exploring and exploiting operations to do both global and local searches well. As a result, many researchers proposed a hybrid algorithm that combines two or more algorithms to improve algorithm search and find optimal path planning with a speed up convergence rate [14,15,16]. Fig.…”

“…As a result, many researchers proposed a hybrid algorithm that combines two or more algorithms to improve algorithm search and find optimal path planning with a speed up convergence rate. [14][15][16]. The proposed method's goal is detecting the security threats in the virtual machines by using the feature selection-based multi-objective method of firefly and harmony search optimization algorithm.…”

The Combined Use of the Wiener Polynomial and SVM for Material Classification Task in Medical Implants Production

“…the developed methods show a high accuracy of the task of identifying the material class, which determines the possibility of their practical use for solving such tasks; the Method 2 should be used for solving classification tasks in the Material Science field in case that doesn't impose restrictions for their training time; the Method 1 shows the greatest accuracy of the solution of the classification task among all the considered ones. It only shows a slightly worse result compared to the basic method for the performance of the training procedure; based on the accuracy and speed of the Method 1 work, it can be used to solve applied classification tasks in the case of large dimensions of the input data; the proposed approach shows a high accuracy of calculating the Wiener polynomial coefficients, which enables its application in the fields of medicine [27], education [28], image processing [29], in particular, for solving tasks of both classification and regression.…”