Measuring Success of Heterogeneous Ensemble Filter Feature Selection Models

Abstract

One problem in utilizing ensemble feature selection models is machine learning is the fact that there is no guarantee that an ensemble model will improve machine learning classification performance. This implies that different ensemble models have different success probability, i.e. have different probability in improving the performance of machine learning. This paper introduces the concept of success probability for heterogeneous ensemble models and stated the definitions, notations, and algorithms necessary to the mathematical formulation and computation of the success probability. To show how the theory applied, we create an ensemble filter feature selection model that uses four filter feature selection algorithms (Correlation, Gain Ratio, Info Gain, and One R) as base filters and the Max as a combination method. The experimental results showed that the success probability of the developed ensemble filter model using a set of 9 machine learning algorithms is found to be 0.58.

Stability and Accuracy of Feature Selection Methods on Datasets of Varying Data Complexity

“…Ensemble Feature Selection Techniques Recently researchers showed more attention to ensemble feature selection techniques by proposing frameworks that combined multiple feature selection methods and aggregate its several outcomes into a single one; in machine learning, this combination is called ensemble learning [3][2] [26]. However, the researchers assumed that using such a technique will provide more accurate and stable results than results produced by a single feature selection method as it generates and aggregates different perspectives about the relevant features [3] [32]. Compared to single-based learning, the authors in the literature emphasised that ensemble learning is a good tool for discovering hidden knowledge related to the important features.…”

“…However, the heterogeneous ensemble technique is a good approach for evaluating the individual-based selectors' strengths and weaknesses [36]. Recent studies using this approach to tackle the stability issue can be found in [32][36] [31].…”