title: Classification of imbalanced bioinformatics data by using boundary movement-based ELM.
abstract: To address the imbalanced classification problem emerging in Bioinformatics, a boundary movement-based extreme learning machine (ELM) algorithm called BM-ELM was proposed. BM-ELM tries to firstly explore the prior information about data distribution by condensing all training instances into the one-dimensional feature space corresponding to the original output in ELM, and then on the transformed space, to find the optimal moving distance of the classification hyperplane by estimating the probability density distributions of the instances in different classes. Experimental results on four real imbalanced bioinformatics classification data sets indicated that the proposed BM-ELM algorithm outperforms some traditional bias correction algorithms due to it can greatly improve the sensitivity of the classification results with small loss of specificity as possible. Also, BM-ELM algorithm has presented better performance than the widely used support vector machine (SVM) classifier. The algorithm can be widely popularized in various large-scale bioinformatics applications.
authors.affiliation: School of Computer Science and Engineering, Jiangsu University of Science and Technology, No. 2 Mengxi Road, Zhenjiang 212003, China.
authors.forename: Ke
authors.initials: K
authors.lastname: Cheng
authors.affiliation: School of Electronic Information, Jiangsu University of Science and Technology, No. 2 Mengxi Road, Zhenjiang 212003, China.
authors.forename: Qingfang
authors.initials: Q
authors.lastname: Chen
authors.affiliation: School of Computer Science and Engineering, Jiangsu University of Science and Technology, No. 2 Mengxi Road, Zhenjiang 212003, China.
authors.forename: Xibei
authors.initials: X
authors.lastname: Yang
authors.affiliation: School of Computer Science and Engineering, Jiangsu University of Science and Technology, No. 2 Mengxi Road, Zhenjiang 212003, China.
authors.forename: Shang
authors.initials: S
authors.lastname: Gao
authors.affiliation: School of Computer Science and Engineering, Jiangsu University of Science and Technology, No. 2 Mengxi Road, Zhenjiang 212003, China.
authors.forename: Hualong
authors.initials: H
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods; D030541:Databases, Genetic; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D000069550:Machine Learning; D008957:Models, Genetic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods*; D017421:Sequence Analysis / Q000379:methods*; D060388:Support Vector Machine
keywords: Bioinformatics; extreme learning machine; imbalanced classification; kernel density estimation