FHSM: Fuzzy Heterogeneous Split Measure algorithm for decision trees

Classification is the best way to partition a given data set. Decision tree is one of the common methods for extracting knowledge from the data set. Traditional decision tree faces the problem of crisp boundary hence fuzzy boundary conditions are proposed in this research. The paper proposes Fuzzy Heterogeneous Split Measure (FHSM) algorithm for decision tree construction that uses trapezoidal membership function to assign fuzzy membership value to the attributes. Size of the decision tree is one of the main concern as larger size leads to incomprehensible rules. The proposed algorithm tries to reduce the size of the decision tree generated by fixing the value of the control variable in this approach without compromising the classification accuracy.