Multi-label symbolic value partitioning through random walks

Feature selection and symbolic value partitioning are effective knowledge reduction techniques in the field of data mining. A large body of feature selection methods has been proposed for multi-label data. By contrast, symbolic value partitioning for such data has not been studied. In this paper, we propose the multi-label symbolic value partitioning through random walks algorithm with two stages. In the first stage, an undirected weighted graph is constructed for each attribute. Each node corresponds to an attribute value and the weight of each edge corresponds to the similarity between two nodes. Similarity is defined based on the attribute value distribution for each label. In the second stage, a random walk algorithm is used to cluster attribute values. The average weight serves as the separation operator to sharpen the inter-cluster edges. We tested the new algorithm and seven popular feature selection algorithms on 13 datasets. The experimental results demonstrate the effectiveness of the proposed algorithm in reducing the data size and improving classification accuracy. (C) 2020 Elsevier B.V. All rights reserved.