Does Classification Using C4.5 Algorithm based on Various Entropies is Effective?

First of All Data mining is an interdisciplinary field of computer science and is referred to extracting or mining knowledge from large amounts of data. Classification is one of the data mining techniques that maps the data into the predefined classes and groups. It is used to predict group membership for data instances. There are many areas that adapt Data mining techniques such as medical, marketing, telecommunications, and stock, health care and so on. The C4.5 can be referred as the statistic Classifier. 

There are five classification method testes by 5 fold cross validation on the eight data set. In Section evaluates the Mean Squared Error of Different Methods, table 3 shows the MSE of each method on each data. Another work in this thesis is to built 3 entropies Shannon, Havrda and Charvt, Quadratic) Based C4.5 that apply all three entropy in parallel for building model and take best of one for Classify data. This technique compares with other machine learning techniques such as C4.5- algorithm, SVM (support vector machine), KNN (K-Nearest Neighbor) etc. Table 4 shows the accuracy of all machines learning technique. 

I think this s experiment is performed over eight real datasets using the five methods namely C4.5 decision tree algorithm based on Shannon Entropy, C4.5 decision tree algorithm based on Havrda and Charvt entropy, C4.5 decision tree algorithm based on Quadratic entropy, C4.5 decision tree algorithm based on R´enyi entropy and C4.5 decision tree algorithm based on Taneja entropy. As shown in table 5, accuracy of Experimental Method based on three entropies is better than C4.5 algorithm. This paper also shows that comparative analysis between machine learning shown in above table.

Entropy Computation is used to create compact decision trees with successful classification. The size of the decision tree, the performance of the classifier is based on the entropy calculation. So the most precise entropy can be applied to the particular classification problem. The different entropies based approach can be applied in any classification problem. Such as detecting faults in industrial application, Medical diagnosis, loan approval, pattern recognition, classifying market trends etc. This thesis is a comparative study based on Shannon, R´enyi, quadratic, Havrda and Charvt, Taneja entropy and it also builds a model that takes Shannon, quadratic, and Havrda and Charvt entropy in parallel and produce more precise classification for data set and a result of this classification is comparable with the other machining learning techniques. This entropy based approach can be applied in real world classification problems.