RW.KNN: A proposed random walk KNN algorithm for multi-label classification

Multi-label classification refers to the problem that predicts each single instance to be one or more labels in a set of associated labels. It is common in many real-world applications such as text categorization, functional genomics and semantic scene classification. The main challenge for multi-label classification is predicting the labels of a new instance with the exponential number of possible label sets. Previous works mainly pay attention to transforming the multi-label classification to be single-label classification or modifying the existing traditional algorithm. In this paper, a novel algorithm which combines the advantage of the famous KNN and Random Walk algorithm (RW.KNN) is proposed. The KNN based link graph is built with the k-nearest neighbors for each instance. For an unseen instance, a random walk is performed in the link graph. The final probability is computed according to the random walk results. Lastly, a novel algorithm based on minimizing Hamming Loss to select the classification threshold is also proposed in this paper.