MultiKOC: Multi-One-Class Classifier Based K-means Clustering

Algorithms

Waleed Khalifa¹  Murad Badarna²  Loai Abdallah²  Malik Yousef³ 

[1] Department of Computer Science, The College of Sakhnin, Sakhnin 3081003, Israel;Department of Information Systems, Yezreel Valley Academic College, Emek Yezreel 1930600, Israel;Department of Information Systems, Zefat Academic College, Safed 1320611, Israel;

关键词: one-class;    clustering based classification;    K-means;   

DOI  :  10.3390/a14050134

来源: DOAJ

【 摘 要 】

In the computational biology community there are many biological cases that are considered as multi-one-class classification problems. Examples include the classification of multiple tumor types, protein fold recognition and the molecular classification of multiple cancer types. In all of these cases the real world appropriately characterized negative cases or outliers are impractical to achieve and the positive cases might consist of different clusters, which in turn might lead to accuracy degradation. In this paper we present a novel algorithm named MultiKOC multi-one-class classifiers based K-means to deal with this problem. The main idea is to execute a clustering algorithm over the positive samples to capture the hidden subdata of the given positive data, and then building up a one-class classifier for every cluster member’s examples separately: in other word, train the OC classifier on each piece of subdata. For a given new sample, the generated classifiers are applied. If it is rejected by all of those classifiers, the given sample is considered as a negative sample, otherwise it is a positive sample. The results of MultiKOC are compared with the traditional one-class, multi-one-class, ensemble one-classes and two-class methods, yielding a significant improvement over the one-class and like the two-class performance.

【 授权许可】

Unknown