Multi-Class Classification Using Improved Mahalanobis-Taguchi System Based on Binary Tree and Its Application - Niu Junlei (Hangkong bingqi)
Aiming at the inadequacy of Mahalanobis-Taguchi System(MTS), an improved MTS optimization model(MTSO) is proposed. The core idea is that a number of optimization objectives are proposed based on the purpose and characteristics of the data classification problem and optimization model is used for screening important variables instead of orthogonal arrays and signal-noise-ratio. And a multi-class classification using improved MTS based on binary tree is proposed. Research the project of building binary tree, which makes a comprehensive consideration of capability of class partition and numbers of samples, according to characteristic of MTS. The experimental results show that the algorithm has a very short classification time and high identification rate, and is an effective multi-classification algorithm.