Title :
FHSM: Fuzzy Heterogeneous Split Measure algorithm for decision trees
Author :
Bajaj, Shalini Bhaskar ; Kubba, Akshaya
Author_Institution :
Dept. of Comput. Sci., G.D. Goenka Univ., Gurgaon, India
Abstract :
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.
Keywords :
classification; decision trees; fuzzy set theory; knowledge acquisition; FHSM; classification; crisp boundary; data set; decision trees; fuzzy boundary conditions; fuzzy heterogeneous split measure algorithm; knowledge extraction; trapezoidal membership function; Accuracy; Classification algorithms; Conferences; Decision trees; Indexes; Machine learning algorithms; Partitioning algorithms; Classification; HSM; fuzzy decision tree; fuzzy membership function;
Conference_Titel :
Advance Computing Conference (IACC), 2014 IEEE International
Conference_Location :
Gurgaon
Print_ISBN :
978-1-4799-2571-1
DOI :
10.1109/IAdCC.2014.6779388