International Journal of Scientific and Research Publications, Volume 3, Issue 12, December 2013 1 ISSN 2250-3153 www.ijsrp.org Survey paper on improved methods of ID3 decision tree classification Shikha Chourasia Computer Engineering Department, 23 Park Road, SGSITS Indore 452003 MP India Abstract- Decision tree classification technique is one of the most popular techniques in the emerging field of data mining. There are various methods for constructing decision tree. Induced Decision tree (ID3) is the basic algorithm for constructing decision trees. After ID3 various algorithms were proposed by different researchers and authors those are extensions of ID3 algorithm. This paper contains a survey about the improved methods of ID3 decision tree classification and those are FID3 (fixed induced decision tree) and VPRSFID3 (variable precision rough set fixed induced decision tree). In this short survey we will investigate which method is best among all the other methods. Index Terms- Decision tree, ID3, FID3, VPRSFID3 I. INTRODUCTION lassification is the prediction approach in data mining techniques. There are many algorithms based on classification that is Instance based, neural networks, Bayesian networks, support vector machine, and decision tree, Decision tree classify instances by sorting them down the tree from the root to some leaf node, which provides the classification of instance. Each node in the tree specifies a test of some attribute of the instance and each branch descending from that node corresponds to one of the possible values for this attribute. Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition, to name only a few. Perhaps, the most important feature of DTC's is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A. Decision tree representation Figure illustrates a typical decision tree [2]. This decision tree classifies the situation of playing tennis according to the weather. For example, the instance Would be sorted down the left most branch of the decision tree and would therefore be classified as a negative instance it means the tree predicts that play tennis = no. Sunny Overcast Rain Yes High normal strong weak No Yes Yes No II. BACKGROUND STUDY A. Rough set theory Rough set theory was proposed [7] by Poland in 1982 is a mathematical tool to deal with vagueness and uncertainty [3] was introduced to process the uncertainty and imprecise information. Here are concepts of rough set theory. 1. Indescernibility relation Let S= (U, C, D, V, f) be a decision table and A=C U D, then with any BϵA there is an equivalence relation IND A (B): IND A (B) is called the B-indescernibility relation, its classes are denoted by [x] B. 2. Set approximation Let T = (U, A) and let and We can approximate X using only the information contained in B by construction the β-lower and β-upper approximations of X, denoted and respectively, where 3. Positive Region Suppose P and Q are the equivalent relationship in U, then the positive region P of Q can be marked as POS p (Q), and POS p (Q) = UP*(Q) If POSp(Q)=POS p-{r} (Q), rϵP, we say that attribute r is omissible in P with respect to Q. Otherwise, r is necessary. C Outlook Humidity Wind