A Clustering Approach for Feature Selection in Microarray Data Classification Using Random Forest


Husna Aydadenta, Adiwijaya, Journal of Information Processing Systems Vol. 14, No. 5, pp. 1167-1175, Oct. 2018

10.3745/JIPS.04.0087
Keywords: Classification, Clustering, Dimensional Reduction, Microarray, Random Forest
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Abstract

Microarray data plays an essential role in diagnosing and detecting cancer. Microarray analysis allows the examination of levels of gene expression in specific cell samples, where thousands of genes can be analyzed simultaneously. However, microarray data have very little sample data and high data dimensionality. Therefore, to classify microarray data, a dimensional reduction process is required. Dimensional reduction can eliminate redundancy of data; thus, features used in classification are features that only have a high correlation with their class. There are two types of dimensional reduction, namely feature selection and feature extraction. In this paper, we used k-means algorithm as the clustering approach for feature selection. The proposed approach can be used to categorize features that have the same characteristics in one cluster, so that redundancy in microarray data is removed. The result of clustering is ranked using the Relief algorithm such that the best scoring element for each cluster is obtained. All best elements of each cluster are selected and used as features in the classification process. Next, the Random Forest algorithm is used. Based on the simulation, the accuracy of the proposed approach for each dataset, namely Colon, Lung Cancer, and Prostate Tumor, achieved 85.87%, 98.9%, and 89% accuracy, respectively. The accuracy of the proposed approach is therefore higher than the approach using Random Forest without clustering.


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Cite this article
[APA Style]
Husna Aydadenta and Adiwijaya (2018). A Clustering Approach for Feature Selection in Microarray Data Classification Using Random Forest. Journal of Information Processing Systems, 14(5), 1167-1175. DOI: 10.3745/JIPS.04.0087.

[IEEE Style]
H. Aydadenta and Adiwijaya, "A Clustering Approach for Feature Selection in Microarray Data Classification Using Random Forest," Journal of Information Processing Systems, vol. 14, no. 5, pp. 1167-1175, 2018. DOI: 10.3745/JIPS.04.0087.

[ACM Style]
Husna Aydadenta and Adiwijaya. 2018. A Clustering Approach for Feature Selection in Microarray Data Classification Using Random Forest. Journal of Information Processing Systems, 14, 5, (2018), 1167-1175. DOI: 10.3745/JIPS.04.0087.