Graph-based Selective Outlier Ensembles

TL;DR

This paper introduces a novel graph-based method for selecting the most effective components in outlier ensemble techniques, improving consensus quality by filtering out poor performers.

Contribution

It proposes a new graph-based ranking selection approach for outlier ensembles, demonstrating superior performance over existing methods through extensive experiments.

Findings

Outperforms state-of-the-art selective outlier ensemble techniques

Effective in heterogeneous data environments

Enhances ensemble consensus quality

Abstract

An ensemble technique is characterized by the mechanism that generates the components and by the mechanism that combines them. A common way to achieve the consensus is to enable each component to equally participate in the aggregation process. A problem with this approach is that poor components are likely to negatively affect the quality of the consensus result. To address this issue, alternatives have been explored in the literature to build selective classifier and cluster ensembles, where only a subset of the components contributes to the computation of the consensus. Of the family of ensemble methods, outlier ensembles are the least studied. Only recently, the selection problem for outlier ensembles has been discussed. In this work we define a new graph-based class of ranking selection methods. A method in this class is characterized by two main steps: (1) Mapping the rankings onto a graph structure; and (2) Mining the resulting graph to identify a subset of rankings. We define a specific instance of the graph-based ranking selection class. Specifically, we map the problem of selecting ensemble components onto a mining problem in a graph. An extensive evaluation was conducted on a variety of heterogeneous data and methods. Our empirical results show that our approach outperforms state-of-the-art selective outlier ensemble techniques.