Abstract:  Feature selection is one of the key problems in machine learning and data mining. It involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. It can reduce the dimensionality of original data, speed up the learning process and build comprehensible learning models with good generalization performance. Nowadays, ensemble idea has been used to improve the performance of feature selection by integrating multiple base feature selection models into an ensemble one. It turned out to be effective in dealing with high dimensionality small sample size problem especially for robust biomarker identification. In this paper, we aim to improve the efficiency of feature selection in dealing with large scale problems. In order to deal with such problems, ensemble feature selection using min-max strategy is proposed. The method consists of three main steps: firstly, the original data is decomposed into a group of relatively smaller balanced ones according to their structure and labels. Secondly, feature selection method is used to deal with all of the sub-problems and obtain the result of feature selection, such as feature weight. Lastly, the final result is obtained by combining the different results of sub-problems according to the min-max strategy. The experiments are designed to compare the Min-Max strategy based ensemble method with three other strategies, namely, Mean-Weight, Voting and K-Medoid, on the accuracy of classification. In this paper, G-Mean evaluation metric is choosed for the reason of considering the imbalance of data. Also, different feature selection algorithms and data decomposition methods are choosed to show the effect of the proposed method. The results, on 5 publicly available real-world datasets, demonstrate that the Min-Max strategy is superior to other ones in most cases and ensemble feature selection using Min-Max strategy can efficiently deal with large scale data.