Globally Optimal Fuzzy Decision Trees for Classification and Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
The minimum-entropy set cover problem
Theoretical Computer Science - Automata, languages and programming: Algorithms and complexity (ICALP-A 2004)
Fuzzy rough sets and multiple-premise gradual decision rules
International Journal of Approximate Reasoning
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Support vector learning for fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
A geometric approach to Support Vector Machine (SVM) classification
IEEE Transactions on Neural Networks
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The Classification of data is usually very large database that is the reason we want to classify the large data into different fragmentation of its same type. Already many algorithms have been used for classification like Id3, rule based algorithm, decision tree based algorithm, k-nearest-neighbor classification and so on. And these algorithm mainly used for classifying the algorithm accurately and the concept of fast classification is lagging behind in the previous algorithms. In this paper we analysis the efficiency and accuracy of using the entropy, id3 and SVM algorithm with our proposed method of using entropy and fuzzy classification with lower and upper approximation to reduce the computation work for more accuracy classification. We use id3 algorithm to classify the complex member that lie between the lower and upper approximation. Now we use SVM algorithm to classify the other data members thus by hybrid of both the algorithm with our approximation we get the best result of the algorithm Fuzzy Fast Classification FFC. The result of experiments shows that the improved fuzzy fast classification algorithm considerably reduces the computational complexity and improves the speed of classification particularly in the circumstances of the large data.