Automatic recommendation of classification algorithms based on data set characteristics

Choosing appropriate classification algorithms for a given data set is very important and useful in practice but also is full of challenges. In this paper, a method of recommending classification algorithms is proposed. Firstly the feature vectors of data sets are extracted using a novel method and the performance of classification algorithms on the data sets is evaluated. Then the feature vector of a new data set is extracted, and its k nearest data sets are identified. Afterwards, the classification algorithms of the nearest data sets are recommended to the new data set. The proposed data set feature extraction method uses structural and statistical information to characterize data sets, which is quite different from the existing methods. To evaluate the performance of the proposed classification algorithm recommendation method and the data set feature extraction method, extensive experiments with the 17 different types of classification algorithms, the three different types of data set characterization methods and all possible numbers of the nearest data sets are conducted upon the 84 publicly available UCI data sets. The results indicate that the proposed method is effective and can be used in practice.

► We propose a novel feature vector to characterize data sets.
► We propose a k-NN based method to support the selection of classification algorithms.
► The novel feature vector outperforms the traditional one in algorithm recommendation.
► The proposed algorithm recommendation method is effective and works well.