uEFS: An efficient and comprehensive ensemble-based feature selection methodology to select informative features

Abstract

Feature selection is considered to be one of the most critical methods for choosing appropriate features from a larger set of items. This task requires two basic steps: ranking and filtering. Of these, the former necessitates the ranking of all features, while the latter involves filtering out all irrelevant features based on some threshold value. In this regard, several feature selection methods with well-documented capabilities and limitations have already been proposed. Similarly, feature ranking is also nontrivial, as it requires the designation of an optimal cutoff value so as to properly select important features from a list of candidate features. However, the availability of a comprehensive feature ranking and a filtering approach, which alleviates the existing limitations and provides an efficient mechanism for achieving optimal results, is a major problem. Keeping in view these facts, we present an efficient and comprehensive univariate ensemble-based feature selection (uEFS) methodology to select informative features from an input dataset. For the uEFS methodology, we first propose a unified features scoring (UFS) algorithm to generate a final ranked list of features following a comprehensive evaluation of a feature set. For defining cutoff points to remove irrelevant features, we subsequently present a threshold value selection (TVS) algorithm to select a subset of features that are deemed important for the classifier construction. The uEFS methodology is evaluated using standard benchmark datasets. The extensive experimental results show that our proposed uEFS methodology provides competitive accuracy and achieved (1) on average around a 7% increase in f-measure, and (2) on average around a 5% increase in predictive accuracy as compared with state-of-the-art methods.

Fig 1. uEFS methodology.

Fig 2. UFS algorithm [19].

Fig 3. TVS algorithm.

Fig 4. An average predictive accuracy graph using the 10-fold cross-validation technique for threshold value identification.

Fig 5. An average predictive accuracy graph using training datasets for threshold value identification.

Fig 6. Predictive accuracies of classifiers against benchmark datasets with varying percentages of retained features.

Fig 7. Comparisons of F-measure with existing FS measures.

Fig 8. Comparisons of F-measure with existing FS measures [29, 37, 39, 48].

Fig 9. Comparisons of predictive accuracy with existing FS measures [29, 37, 39, 48].