期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：3
页码：767
出版社：Journal of Theoretical and Applied
摘要：This paper presents a method of membrane protein feature extraction using a combination of the local discriminant bases (LDB) and three different classifiers. This method has adopted two dissimilarity measures of normalized energy difference and relative entropy to identify a set of orthogonal subspaces in optimal wavelet packets. The energy will be derived from the calculation of the two dissimilarity measures that have overlapping subspaces. This feature, in turn, serves as an input to support vector machine (SVM), decision tree and naïve Bayes classifiers. The proposed model yields the highest accuracy of 78.6%, 76.25%, 76.72% for dataset S1, S2, and S3 respectively by using SVM. This technique outperformed other feature extraction method for membrane protein type classification for dataset S2 and S3.
关键词：Membrane Proteins; Feature Extraction; Local Discriminant Bases; Wavelet; SVM
