Local feature selection for multiple instance learning

摘要

We propose a local feature selection method for the Multiple Instance Learning (MIL) framework. Unlike conventional feature selection algorithms that assign a global set of features to the whole data set, our algorithm, called Multiple Instance Local Salient Feature Selection (MI-LSFS), searches the feature space to find the relevant features within each bag. We also propose a new multiple instance classification algorithm, called Multiple Instance Learning via Embedded Structures with Local Feature Selection (MILES-LFS), by integrating the information learned by MI-LSFS during the feature selection process. In MILES-LFS, we use information learned by MI-LSFS to identify a reduced subset of representative bags. For each representative bag, we identify its most representative instances. Using the instance prototypes of all representative bags and their relevant features, we project and map the MIL data to a standard feature vector data. Finally, we train a 1-Norm support vector machine (1-Norm SVM) to learn the classifier. We investigate the performance of MI-LSFS in selecting the local relevant features using synthetic and benchmark data sets. The results confirm that MI-LSFS can identify the relevant features for each bag. We also investigate the performance of the proposed MILES-LFS algorithm on several synthetic and real benchmark data sets. The results confirm that MILES-LFS has a robust classification performance comparable to the well-known MILES algorithm. More importantly, our results confirm that using the reduced set of prototypes to project the MIL data reduces the computational time significantly without affecting the classification accuracy.