Large-Scale Multi-Modality Attribute Reduction with Multi-Kernel Fuzzy Rough Sets

Abstract-In complex pattern recognition tasks, objects are typically characterized by means of multi-modality attributes, including categorical, numerical, text, image, audio and even videos. In these cases, data are usually high-dimensional, structurally complex, and granular. Those attributes exhibit some redundancy and irrelevant information. The evaluation, selection, and combination of multi-modality attributes pose great challenges to traditional classification algorithms. Multi-kernel learning handles multi-modality attributes by using different kernels to extract information coming from different attributes. However, it cannot consider the aspects fuzziness in fuzzy classification. Fuzzy rough sets emerge as a powerful vehicle to handle fuzzy and uncertain attribute reduction. In this paper, we design a framework of multi-modality attribute reduction based on multi-kernel fuzzy rough sets. First, a combination of kernels based on set theory is defined to extract fuzzy similarity for fuzzy classification with multi-modality attributes. Then, a model of multi-kernel fuzzy rough sets is constructed. Finally, we design an efficient attribute reduction algorithm for large scale multi-modality fuzzy classification based on the proposed model. Experimental results demonstrate the effectiveness of the proposed model and the corresponding algorithm