The application of one-class machine learning is gaining attention in the computational biology community. Many biological cases can be considered as multi one-class classification problem. Examples include the classification of multiple cancer types, protein fold recognition and, molecular classification of multiple tumor types. In all of those cases the real world appropriately characterized negative cases or outliers are impractical to be achieved and the positive cases might be consists from different clusters which in turn might reveal to accuracy degradation. In this paper, we present multi-one-class classifier to deal with this problem. The key point of our classification method is to run a clustering algorithm such as the well-known k-means over the positive cases and then building up a classifier for every cluster separately. For a given new example, we apply all the generated classifiers. If it rejected by all of those classifiers, the given example will be considered as a negative case, otherwise it is a positive case.
|Number of pages||9|
|Journal||Communications in Computer and Information Science|
|State||Published - 2019|
Bibliographical notePublisher Copyright:
© Springer Nature Switzerland AG 2019.
- Clustering based classification
- Ensemble clustering
- One class SVM
ASJC Scopus subject areas
- Computer Science (all)
- Mathematics (all)