An empirical comparison of min–max-modular k-NN with different voting methods to large-scale text categorization

Text categorization refers to the task of assigning the pre-defined classes to text documents based on their content. k-NN algorithm is one of top performing classifiers on text data. However, there is little research work on the use of different voting methods over text data. Also, when a huge number of training data is available online, the response speed slows down, since a test document has to obtain the distance with each training data. On the other hand, min–max-modular k-NN (M3-k-NN) has been applied to large-scale text categorization. M3-k-NN achieves a good performance and has faster response speed in a parallel computing environment. In this paper, we investigate five different voting methods for k-NN and M3-k-NN. The experimental results and analysis show that the Gaussian voting method can achieve the best performance among all voting methods for both k-NN and M3-k-NN. In addition, M3-k-NN uses less k-value to achieve the better performance than k-NN, and thus is faster than k-NN in a parallel computing environment.