Hybrid Filter and Genetic Algorithm-Based Feature Selection for Improving Cancer Classification in High-Dimensional Microarray Data

Ali, Waleed and Saeed, Faisal (2023) Hybrid Filter and Genetic Algorithm-Based Feature Selection for Improving Cancer Classification in High-Dimensional Microarray Data. Processes, 11 (2). p. 562. ISSN 2227-9717

processes-11-00562-v2.pdf - Published Version
Available under License Creative Commons Attribution.

Download (1MB)


The advancements in intelligent systems have contributed tremendously to the fields of bioinformatics, health, and medicine. Intelligent classification and prediction techniques have been used in studying microarray datasets, which store information about the ways used to express the genes, to assist greatly in diagnosing chronic diseases, such as cancer in its earlier stage, which is important and challenging. However, the high-dimensionality and noisy nature of the microarray data lead to slow performance and low cancer classification accuracy while using machine learning techniques. In this paper, a hybrid filter-genetic feature selection approach has been proposed to solve the high-dimensional microarray datasets problem which ultimately enhances the performance of cancer classification precision. First, the filter feature selection methods including information gain, information gain ratio, and Chi-squared are applied in this study to select the most significant features of cancerous microarray datasets. Then, a genetic algorithm has been employed to further optimize and enhance the selected features in order to improve the proposed method’s capability for cancer classification. To test the proficiency of the proposed scheme, four cancerous microarray datasets were used in the study—this primarily included breast, lung, central nervous system, and brain cancer datasets. The experimental results show that the proposed hybrid filter-genetic feature selection approach achieved better performance of several common machine learning methods in terms of Accuracy, Recall, Precision, and F-measure.

Item Type: Article
Identification Number: https://doi.org/10.3390/pr11020562
10 February 2023Accepted
12 February 2023Published Online
Uncontrolled Keywords: cancer classification, filter feature selection, genetic algorithm, gene selection, microarray dataset
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Gemma Tonks
Date Deposited: 09 Mar 2023 10:39
Last Modified: 09 Mar 2023 10:39
URI: https://www.open-access.bcu.ac.uk/id/eprint/14234

Actions (login required)

View Item View Item


In this section...