Learning a fuzzy decision tree from uncertain data

© 2017 IEEE. Uncertainty in data exists when the value of a data item is not a precise value, but rather by an interval data with a probability distribution function, or a probability distribution of multiple values. Since there are intrinsic differences between uncertain and certain data, it is difficult to deal with uncertain data using traditional classification algorithms. Therefore, in this paper, we propose a fuzzy decision tree algorithm based on a classical ID3 algorithm, it integrates fuzzy set theory and ID3 to overcome the uncertain data classification problem. Besides, we propose a discretization algorithm that enables our proposed Fuzzy-ID3 algorithm to handle the interval data. Experimental results show that our Fuzzy-ID3 algorithm is a practical and robust solution to the problem of uncertain data classification and that it performs better than some of the existing algorithms

Algoritma Iterative Dichotomiser 3 (Id3) Untuk Mengidentifikasi Data Rekam Medis (Studi Kasus Penyakit Diabetes Mellitus Di Balai Kesehatan Kementerian Perindustrian, Jakarta)

Iterative Dichotomiser 3 (ID3) Algorithm is a basic decision tree learning algorithm. These algorithms perform a thorough search (greedy) in all possible decision tree. ID3 algorithm can be implemented using a recursive function, (function that calls itself). One of the problems that can be solved using the ID3 algorithm is a classification of diabetic patients. Diabetic is a disease because of the body is not able to control the amount of sugar or glucose in the bloodstream. Classification using ID3 in the case of diabetics produce trees with many vertices to 32 knot where 21 of them is a leaf node and attribute two-hour postprandial glucose fasting elected as the root node in the decision-making tree. Based on the classification performance measurements show that the classification accuracy or measurement accuracy reaches 89,75%. While the measurement accuracy of the classification algorithm ID3 using test samples totaling 84 samples showed an accuracy of 72,619%

EVALUATING RISK FACTORS OF BEING OBESE, BY USING ID3 ALGORITHM IN WEKA SOFTWARE

Id3 algorithm is used for building a decision tree from a fixed set of examples, next step is an iterative way that uses the resulting tree to classify future examples. Nowdays the large amount of data needs to be classified into useful information. Being obese refers to an excessive accumulation of body fat. The aim of this paper is to construct a decision tree with Id3 algorithm, by the data collect from Tirana Inter-medical Centre, analyzing the factors that makes the patients obese

EVALUATING RISK FACTORS OF BEING OBESE, BY USING ID3 ALGORITHM IN WEKA SOFTWARE

Id3 algorithm is used for building a decision tree from a fixed set of examples, next step is an iterative way that uses the resulting tree to classify future examples. Nowdays the large amount of data needs to be classified into useful information. Being obese refers to an excessive accumulation of body fat. The aim of this paper is to construct a decision tree with Id3 algorithm, by the data collect from Tirana Inter-medical Centre, analyzing the factors that makes the patients obese

Attribute related methods for improvement of ID3 Algorithm in classification of data: A review

Decision tree is an important method in data mining to solve the classification problems. There are several learning algorithms to implement the decision tree but the most commonly-used is ID3 algorithm. Nevertheless, there are some limitations in ID3 algorithm that can affect the performance in the classification of data. The use of information gain in the ID3 algorithm as the attribute selection criteria is not to assess the relationship between classification and the dataset’s attributes. The objective of the study being conducted is to implement the attribute related methods to solve the shortcomings of the ID3 algorithm like the tendency to select attributes with many values and also improve the performance of ID3 algorithm. The techniques of attribute related methods studied in this paper were mutual information, association function and attribute weighted. All the techniques assist the decision tree to find the most optimal attributes in each generation of the tree. Results of the reviewed techniques show that attribute selection methods capable to resolve the limitations in ID3 algorithm and increase the performance of the method. All of the reviewed techniques have their advantages and disadvantages and useful to solve the classification problems. Implementation of the techniques with ID3 algorithm is being discussed thoroughly

An extended ID3 decision tree algorithm for spatial data

Utilizing data mining tasks such as classification on spatial data is more complex than those on non-spatial data. It is because spatial data mining algorithms have to consider not only objects of interest itself but also neighbours of the objects in order to extract useful and interesting patterns. One of classification algorithms namely the ID3 algorithm which originally designed for a non-spatial dataset has been improved by other researchers in the previous work to construct a spatial decision tree from a spatial dataset containing polygon features only. The objective of this paper is to propose a new spatial decision tree algorithm based on the ID3 algorithm for discrete features represented in points, lines and polygons. As in the ID3 algorithm that use information gain in the attribute selection, the proposed algorithm uses the spatial information gain to choose the best splitting layer from a set of explanatory layers. The new formula for spatial information gain is proposed using spatial measures for point, line and polygon features. Empirical result demonstrates that the proposed algorithm can be used to join two spatial objects in constructing spatial decision trees on small spatial dataset. The proposed algorithm has been applied to the real spatial dataset consisting of point and polygon features. The result is a spatial decision tree with 138 leaves and the accuracy is 74.72%