The effectiveness of using diversity to select multiple classifier systems with varying classification thresholds

Abstract

In classification applications, the goal of fusion techniques is to exploit complementary approaches and merge the information provided by these methods to provide a solution superior than any single method. Associated with choosing a methodology to fuse pattern recognition algorithms is the choice of algorithm or algorithms to fuse. Historically, classifier ensemble accuracy has been used to select which pattern recognition algorithms are included in a multiple classifier system. More recently, research has focused on creating and evaluating diversity metrics to more effectively select ensemble members. Using a wide range of classification data sets, methodologies, and fusion techniques, current diversity research is extended by expanding classifier domains before employing fusion methodologies. The expansion is made possible with a unique classification score algorithm developed for this purpose. Correlation and linear regression techniques reveal that the relationship between diversity metrics and accuracy is tenuous and optimal ensemble selection should be based on ensemble accuracy. The strengths and weaknesses of popular diversity metrics are examined in the context of the information they provide with respect to changing classification thresholds and accuracies.

Linear iterative feature embedding: an ensemble framework for an interpretable model

“…Empirically, ensembles tend to yield better results than a single model when there is a significant diversity among the models [17]. For the past few decades, many studies have been focusing on accuracy and diversity of ensemble methods in either regression [18] or classification case [19][20][21]. (Krogh and Vedelsby (1994) [22]) proposed ambiguity decomposition and a computable approach to minimize the quadratic error of the ensemble estimator, while (Ueda and Nakano (1996) [23]) derived a general expression of bias-variance-covariance decomposition.…”

Linear Iterative Feature Embedding: An Ensemble Framework for Interpretable Model

“…Empirically, ensembles tend to yield better results than a single model when there is a significant diversity among the models [18]. For the past few decades, many studies have been focusing on accuracy and diversity of ensemble methods in either regression [6] or classification case [1,11,7]. (Krogh and Vedelsby (1994) [15]) proposed ambiguity decomposition and a computable approach to minimize the quadratic error of the ensemble estimator, while (Ueda and Nakano (1996) [25]) derived a general expression of bias-variance-covariance decomposition.…”

Distributed sequential estimation procedures

“…It follows that how to stably divide a data set into reasonable partitions without introducing bias is a problem of concern. For DC‐type methods, how to combine results from individual partitions into a useful result is an essential problem, especially when results from individual partitions are inconsistent (Wang, 2008; Butler et al, 2018; Yu, 2018). Milicchio & Gehrke (2007) discussed the pros and cons of different architectures for building and maintaining a large cluster.…”