The Extension Of Ordinal Decision Tree In The SOCA And Fuzzy Ordinal Decision Tree

A simple ordinal classification approach(SOCA) has been proposed by Frank and Hall. SOCA is a general method, any classification algorithm that can output class probability estimates, such as C4.5, k nearest neighbors(KNN) algorithm and extreme learning machine(ELM) algorithm etc. can be applied to this approach to deal with the problem of ordinal classification. We find that in SOCA only ordering information of decision attribute is used to classify objects but the ordering information of conditional attributes is not considered. However, we experimentally find that ordering information of conditional attributes can also improve the generalization ability of the classification algorithm. In order to deal the problem mentioned above, in this paper, we propose an improved ordinal classification methodology by employing the ordering information of both conditional and decision attributes. In addition, we analyze the sensitivity of the SOCA on performance to the underlying classification algorithms, for instance, C4.5, KNN and ELM.In addition, the proposed ordinal decision tree algorithm is extended to fuzzy environment, and a fuzzy classification algorithm is proposed in this paper. Some experiments are conducted to analyze the performance of the proposed algorithm, the experimental results show that the proposed algorithms are effective and efficient.