Scalable decision tree based on fuzzy partitioning and an incremental approach

Classification as a data mining materiel is the process of assigning entities to an already defined class by examining the features. The most significant feature of a decision tree as a classification method is its ability to data recursive partitioning. To choose the best attributes for partition, the value range of each continuous attribute should be divided into two or more intervals. Fuzzy partitioning can be used to reduce noise sensitivity and increase the stability of trees. Also, decision trees constructed with existing approaches, tend to be complex, and consequently are difficult to use in practical applications. In this article, a fuzzy decision tree has been introduced that tackles the problem of tree complexity and memory limitation by incrementally inserting data sets into the tree. Membership functions are generated automatically. Then fuzzy information gain is used as a fast-splitting attribute selection criterion and the expansion of a leaf is done attending only with the instances stored in it. The efficiency of this algorithm is examined in terms of accuracy and tree complexity. The results show that the proposed algorithm by reducing the complexity of the tree can overcome the memory limitation and make a balance between accuracy and complexity.