In this article, we describe a feature selection algorithm which can automatically find relevant features for multiple instance learning. Multiple instance learning is considered an extension of traditional supervised learning where each example is made up of several instances and there is no specific information about particular instance labels. In this scenario, traditional supervised learning can not be applied directly and it is necessary to design new techniques. Our approach is based on principles of the well-known Relief-F algorithm which is extended to select features in this new learning paradigm by modifying the distance, the difference function and computation of the weight of the features. Four different variants of this algorithm are proposed to evaluate their performance in this new learning framework. Experiment results using a representative number of different algorithms show that predictive accuracy improves significantly when a multiple instance learning classifier is learnt on the reduced data set.
|Title of host publication
|Hybrid Artificial Intelligence Systems (5th International Symposium, HAIS 2010, San Sebastián, Spain, June 23-25, 2010. Proceedings, Part II)
|E. Corchado, M. Graña Romay, A. Manhaes Savio
|Place of Publication
|Published - 2010
|Lecture Notes in Computer Science