10.35940/ijmh.I0904.0641020
https://zenodo.org/records/5442846
oai:zenodo.org:5442846
Isah Aliyu Kargi
Isah Aliyu Kargi
Department of Mathematics, Faculty of Science, Universiti Teknologi Malaysia 81310 UTM Skudai, Johor, Malaysia. 2 Department of Mathematics and statistics Nuhu Bamalli Polytechnic p.m.b 1061, Zaria
Norazlina Bint Ismail
Norazlina Bint Ismail
Department of Mathematics, Faculty of Science, Universiti Teknologi Malaysia 81310 UTM Skudai, Johor, Malaysia..
Ismail Bin Mohamad
Ismail Bin Mohamad
Department of Mathematics, Faculty of Science, Universiti Teknologi Malaysia 81310 UTM Skudai, Johor, Malaysia.
Maximal Covariance Complexity-Based Penalized Likelihood Method in High Dimensional Data
Zenodo
2020
Lasso, Maximal Complexity, Information Measure, Theoretic Measure, Penalized Likelihood Method, Scale-Invariant Complexity.
Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
Publisher
2020-06-30
eng
2394-0913
Creative Commons Attribution 4.0 International
Classification of cancer and selection of genes is one of the most important application of DNA microarray data. As a result of the higher dimensionality of microarray data, classification and selection of gene techniques are frequently employed to support the professional systems in the diagnosing ability of cancer with higher precision in classification. Least absolute shrinkage and selection operator (LASSO) is one of the most popular method for cancer classification and gene selection in high dimensional data. However, Lasso has limitations of being biased and cannot select variables more than the sample size (n) in gene selection and classification of high dimensional microarray data. To address this problems, LASSO-C1F was proposed using scale invariant measure of maximal information complexity of covariance matrix denoted with weight modifications as data-adaptive alternative to the fairly arbitrary choice of the regularization term in the least absolute shrinkage and selection operator (LASSO). The results indicated the effectiveness of the proposed method LASSO-C1F over the classical LASSO. The evaluation criteria result shows that the proposed method, LASSO-C1F has a better performance in terms of AUC and number of genes selected.