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Item Comparative Analysis of Decision Tree Classification Algorithms(Department of Computer Science and Information Technology, 2014) Karmachrya, ShikhaIn our daily life there is lots of records, phone call records, salary records, homework records, assignment record, personal details record, sales record, song, videos and so on. These all records kept in a table are called data; we have lots of data in different field. Whenever there is data we can have lots of information, patterns, meaning etc. Data mining applications has got rich focus due to its significance of classification algorithms. The comparison of classification algorithm is a complex and it is an open problem. First, the notion of the performance can be defined in many ways: accuracy, speed, cost, reliability, etc. Second, an appropriate tool is necessary to quantify this performance. Third, a consistent method must be selected to compare with the measured values. The selection of the best classification algorithm for a given dataset is a very widespread problem. In this sense it requires to make several methodological choices. So this research focused in the analysis of decision tree classification algorithm in different datasets of multiple attributes and multiple instances. Where analysis was done among five decision tree algorithms (BFTree, J48, Random Tree, REP Tree and Simple Cart).J 48 was able to classify 82.16% of the data correctly which was best among all in comparison to results of evaluation metrics (Accuracy, Precision, Recall and F-Measure) and Simple Cart was able to build decision tree with small tree size of 17.24 (averaged value). Keywords: BF Tree,CART, Data Mining, Decision Tree, J48,Random Tree, REP Tree.Item Performance Analysis of Filter Based Feature Selection Techniques in Tree Based Classification Methods(Department of Computer Science & Information Technology, 2017) Sapkota, MeghrajClassification has been called the most influential development in Data Mining and Machine Learning in the past decade. The idea of classification is to find the class of the unknown objects based on their attributes. In this thesis, the performance of decision tree based classification methods is analysed with feature selection methods; Chi-square and Relief. The feature selection process chooses optimal subset of features according to objective function. These feature selection method helps to remove unnecessary attributes from the high dimensional dataset, thus improves the efficiency of the classification algorithms. The performance of feature section methods; Chisquare and Relief were compared in Tree based classification methods; C4.5, CART, LMT and Random Tree. The study shows that the Chi-square feature selection method is more suitable while using with LMT followed by C4.5, Random Tree and CART respectively. In case Relief based feature selection method LMT gives the best result followed by CART, C4.5 and Random Tree respectively.