Stable feature selection based on instance learning, redundancy elimination and efficient subsets fusion

Feature selection is frequently used as a preprocessing step to data mining and is attracting growing attention due to the increasing amounts of data emerging from different domains. The large data dimensionality increases the noise and thus the error of learning algorithms. Filter methods for feature selection are specially very fast and useful for high-dimensional datasets. Existing methods focus on producing feature subsets that improve predictive performance, but they often suffer from instability. Instance-based filters, for example, are considered as one of the most effective methods that rank features based on instances neighborhood. However, as the feature weight fluctuates with the instances, small changes in training data result in a different selected subset of features. By another hand, some other filters generate stable results but lead to a modest predictive performance. The absence of a trade-off between stability and classification accuracy decreases the reliability of the feature selection results. In order to deal with this issue, we propose filter methods that improve stability of feature selection while preserving an optimal predictive accuracy and without increasing the complexity of the feature selection algorithms. The proposed approaches first use the strength of instance learning to identify initial sets of relevant features, and the advantage of aggregation techniques to increase the stability of the final set in a second stage. Two classification algorithms are used to evaluate the predictive performance of our proposed instance-based filters compared to state-of-the-art algorithms. The obtained results show the efficiency of our methods in improving both classification accuracy and feature selection stability for high-dimensional datasets.