Improve multi-instance neural networks through feature selection

bMulti-instance learning is regarded as a new learning framework where the training examples are bags composed of instances without labels, and the task is to predict the labels of unseen bags through analyzing the training bags with known labels. Recently, a multi-instance neural network BP-MIP was proposed. In this paper, BP-MIP is improved through adopting two different feature selection techniques, i.e. feature scaling with Diverse Density and feature reduction with principal component analysis. In detail, before feature vectors are fed to a BP-MIP neural network, they are scaled by the feature weights found by running Diverse Density on the training data, or projected by a linear transformation matrix formed by principal component analysis. Experiments show that these feature selection mechanisms can significantly improve the performance of BP-MIP.