Multi-instance learning based on representative instance and feature mapping

In this paper, based on the representative instances and feature mapping, we propose two Multi-Instance Learning (MIL) algorithms, i.e. Representative Instance and Feature Mapping for instances (RIFM-I) and Representative Instance and Feature Mapping for bags (RIFM-B). These two algorithms firstly select representative positive and negative instances from positive and negative bags, respectively, and then map selected instances and bags to the feature space, in which MIL problem is converted into conventional single-instance learning problem. Finally, Support Vector Data Description (SVDD) method is introduced to solve the converted problem. The experiment on the MUSK dataset shows that RIFM-I performs better than RIFM-B and provides highest classification accuracies compared with the best results obtained among all the methods, and RIFM-B achieves a competitive average accuracy performance. Furthermore, RIFM-I is applied on COREL image repository for the content-based image retrieval. The experimental results show that RIFM-I outperforms the other image retrieval methods, such as MILES and MissSVM, and is able to distinguish two easily confused categories, Beach and Mountains, quite well. In addition, The results in ten data sets commonly used in MIL also show that RIFM-I can achieve better results in most cases.