Cluster-Based One-Class Ensemble for Classification Problems in Information Retrieval

摘要

A number of relevant information retrieval classification problems are one-class classification problems at heart. I.e., labeled data is only available for one class, the so-called target class, and common discrimination-based classification approaches, be them binary or multiclass, are not applicable. Achieving a, high effectiveness when solving one-class problems is difficult anyway and it becomes even more challenging when the target class data is multimodal, which is often the case. To address these concerns we propose a cluster-based one-class ensemble that consists of four steps: (1) applying a clustering algorithm to the target class data, (2) training an individual one-class classifier for each of the identified dusters, (3) aggregating the decisions of the individual classifiers, and (4) selecting the best fitting clustering model. We evaluate our approach with four datasets: an artificially generated dataset, a dataset compiled from a known multiclass text corpus, and two datasets related to one-class problems that; received much attention recently, namely authorship verification and quality flaw prediction. Our approach outperforms a one-class SVM on all four datasets.