Combining One-Class Classifiers via Meta-Learning
Selecting the best classifier among the available ones is a difficult task, especially when only instances of one class exist. In this work we examine the notion of combining one-class classifiers as an alternative for selecting the best classifier. In particular, we propose two new one-class classification performance measures to weigh classifiers and show that a simple ensemble that implements these measures can outperform the most popular one-class ensembles. Furthermore, we propose a new one-class ensemble scheme, TUPSO, which uses meta-learning to combine one-class classifiers. Our experiments demonstrate the superiority of TUPSO over all other tested ensembles and show that the TUPSO performance is statistically indistinguishable from that of the hypothetical best classifier.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationMeta-LearningOne-Class ClassificationSimilar Papers 제목 키워드 기반
Aggregation of Classifiers: A Justifiable Information Granularity Approach
In this study, we introduce a new approach to combine multi-classifiers in an ensemble system. Instead of using numeric membership values encountered in fixed combining rules, we construct interval membership values asso…
DiversityRanking and combining multiple predictors without labeled data
In a broad range of classification and decision making problems, one is given the advice or predictions of several classifiers, of unknown reliability, over multiple questions or queries. This scenario is different from …
Decision MakingA Hybrid Algorithm for Metaheuristic Optimization
We propose a novel, flexible algorithm for combining together metaheuristicoptimizers for non-convex optimization problems. Our approach treatsthe constituent optimizers as a team of complex agents that communicateinform…
General ClassificationMetaheuristic OptimizationUnsupervised Task Design to Meta-Train Medical Image Classifiers
Meta-training has been empirically demonstrated to be the most effective pre-training method for few-shot learning of medical image classifiers (i.e., classifiers modeled with small training sets). However, the effective…
ClassificationFew-Shot LearningGeneral ClassificationA DEEP analysis of the META-DES framework for dynamic selection of ensemble of classifiers
Dynamic ensemble selection (DES) techniques work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to classify a given test sample. Hence, the …
Meta-Learning