Recently, besides the performance, the stability (robustness, i.e., the variation in feature selection results due to small changes in the data set) of feature selection is received more attention. Ensemble feature selection where multiple feature selection outputs are combined to yield more robust results without sacrificing the performance is an effective method for stable feature selection. ...

Robustness of feature selection techniques is a topic of recent interest, especially in high dimensional domains with small sample sizes, where selected feature subsets are subsequently analysed by domain experts to gain more insight into the problem modelled. In this work, we investigate the robustness of various feature selection techniques, and provide a general scheme to improve robustness ...

Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. It was shown theoretically and experimentally that in order for an ensemble to be effective, it should consist of high-accuracy base classifiers that should have high diversity in their pred...

Recursive Feature Elimination (RFE) combined with feature ranking is an effective technique for eliminating irrelevant features when the feature dimension is large, but it is difficult to distinguish between relevant and redundant features. The usual method of determining when to stop eliminating features is based on either a validation set or cross-validation techniques. In this paper, we pres...

High dimensionality is a major problem that affects the quality of training datasets and therefore classification models. Feature selection is frequently used to deal with this problem. The goal of feature selection is to choose the most relevant and important attributes from the raw dataset. Another major challenge to building effective classification models from binary datasets is class imbal...

In credit risk evaluation the accuracy of a classifier is very significant for classifying the high-risk loan applicants correctly. Feature selection is one way of improving the accuracy of a classifier. It provides the classifier with important and relevant features for model development. This study uses the ensemble of multiple feature ranking techniques for feature selection of credit data. ...

In this paper, a novel method is proposed to build an ensemble of classifiers by using a feature selection schema. The feature selection schema identifies the best feature sets that affect the arrhythmia classification. Firstly, a number of feature subsets are extracted by applying the feature selection schema to the original dataset. Then classification models are built by using the each featu...

Robustness or stability of feature selection techniques is a topic of recent interest, and is an important issue when selected feature subsets are subsequently analysed by domain experts to gain more insight into the problem modelled. In this work, we investigate the use of ensemble feature selection techniques, where multiple feature selection methods are combined to yield more robust results....

Feature selection is an important step when building a classifier. However, the feature selection tends to be unstable on high-dimension and small-sample size data. This instability reduces the usefulness of selected features for knowledge discovery: if the selected feature subset is not robust, domain experts can have little trust that they are relevant. A growing number of studies deal with f...