A new classification algorithm recommendation method based on link prediction

Recommending appropriate classification algorithm(s) for a given classification problem is of great significance and also one of the challenging problems in the field of data mining, which is usually viewed as a meta-learning problem. Multi-label learning has been adopted and validated to be an effective meta-learning method in classification algorithm recommendation. However, the multi-label learning method used in previous classification algorithm recommendation relies only on relationship between data sets and their direct neighbours, ignoring the impact of other data sets. In this paper, a new classification algorithm recommendation method based on link prediction between data sets and classification algorithms is proposed. Taking advantage of link prediction in heterogeneous networks, this method considers the impact of all data sets and makes full use of the interactions between data sets as well as between data sets and algorithms. Firstly, meta data of the training data sets is collected. And then a heterogeneous network called DAR (Data and Algorithm Relationship) Network is constructed with the meta data. Finally, the link prediction technique is adopted to recommend appropriate algorithm(s) for a given data set on the basis of the DAR Network. To evaluate the proposed link prediction-based recommendation method, extensive experiments with 131 data sets and 21 classification algorithms are conducted. Results of 5 performance measures indicate that the proposed method is more effective compared with the base line classification algorithm recommendation method and can be used in practice.