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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제14권 제5호
- 발행연도
- 2018.1
- 수록면
- 1,167 - 1,175 (9page)
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초록· 키워드
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Microarray data plays an essential role in diagnosing and detecting cancer. Microarray analysis allows theexamination of levels of gene expression in specific cell samples, where thousands of genes can be analyzedsimultaneously. However, microarray data have very little sample data and high data dimensionality. Therefore, to classify microarray data, a dimensional reduction process is required. Dimensional reductioncan eliminate redundancy of data; thus, features used in classification are features that only have a highcorrelation with their class. There are two types of dimensional reduction, namely feature selection andfeature 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, sothat redundancy in microarray data is removed. The result of clustering is ranked using the Relief algorithmsuch that the best scoring element for each cluster is obtained. All best elements of each cluster are selectedand used as features in the classification process. Next, the Random Forest algorithm is used. Based on thesimulation, the accuracy of the proposed approach for each dataset, namely Colon, Lung Cancer, and ProstateTumor, achieved 85.87%, 98.9%, and 89% accuracy, respectively. The accuracy of the proposed approach istherefore higher than the approach using Random Forest without clustering.
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참고문헌 (13)
참고문헌 신청
American Cancer Society / 2015 / Cancer Facts & Figures 2015 / American Cancer Society
A. Nurfalah / 2015 / Cancer detection based on microarray data classification using PCA And modified back propagation / Far East Journal of Electronics and Communications 16(2):269~281
K. Moorthy / 2012 / Knowledge Technology / Springer :174~183
E. Pashaei / 2015 / A novel gene selection algorithm for cancer identification based on random forest and particle swarm optimization / Proceedings of 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) :1~6
Z. M. Hira / 2015 / A review of feature selection and feature extraction methods applied on microarray data / Advances in Bioinformatics 2015
A. Nurfalah / 2015 / Cancer detection based on microarray data classification using PCA And modified back propagation / Far East Journal of Electronics and Communications 16(2):269~281
K. Moorthy / 2012 / Knowledge Technology / Springer :174~183
E. Pashaei / 2015 / A novel gene selection algorithm for cancer identification based on random forest and particle swarm optimization / Proceedings of 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) :1~6
Z. M. Hira / 2015 / A review of feature selection and feature extraction methods applied on microarray data / Advances in Bioinformatics 2015
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