A fuzzy rule-based system design concentrates on accuracy and interpretability of the system. Fuzzy decision tree method is proposed based on fuzzy RDBMS and rule generation based on C4.5 algorithm known as fuzzy rule generation system (FRGS) algorithm. A fuzzy decision tree is developed by first converting a medical application of heart relational database to fuzzy heart relational database an...

This paper introduces a fuzzy decision tree to initiate the first population of a genetic algorithm to perform data classification. On large datasets, the evolutive process tends to waste computational resources until some good individual is found. It is expected that the use of a fuzzy decision tree can significantly reduce this feature. The genetic algorithm aims to obtain small fuzzy classif...

Decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Over the years, additional methodologies have been investigated and propos...

A fuzzy knowledge-based network is developed based on the linguistic rules extracted from a fuzzy decision tree. A scheme for automatic linguistic discretization of continuous attributes, based on quantiles, is formulated. A novel concept for measuring the goodness of a decision tree, in terms of its compactness (size) and efficient performance, is introduced. Linguistic rules are quantitativel...

This paper proposes input selection methods for fuzzy modeling, which are based on decision tree search approaches. The branching decision at each node of the tree is made based on the accuracy of the model available at the node. We propose two different approaches of decision tree search algorithms: bottom-up and top-down and four different measures for selecting the most appropriate set of in...

The paper aims at developing a hierarchical algorithm for matching a given template of × on an image of × pixels partitioned into equal sized blocks of × pixels. The algorithm employs a fuzzy metric to measure the dispersion of individual feature of a block with respect to that of the template. A fuzzy threshold, preset by the user, is employed to restrict less likely blocks from participation ...

Crisp decision trees are one of the most popular classification algorithms in current use within data mining and machine learning. However, although they possess many desirable features, they lack the ability to model vagueness. As a result of this, the induction of fuzzy decision trees (FDTs) has become an area of much interest. One important aspect of tree induction is the choice of feature a...

Risk response planning is one of the main phases in the project risk management and has major impacts on the success of a large-scale project. Since projects are unique, and risks are dynamic through the life of the projects, it is necessary to formulate responses of the important risks. The conventional approaches tend to be less effective in dealing with the impreciseness of risk response p...

The most important challenges in electric load forecasting is to find the accurate electricity load forecasting. Because, it is volatile in nature and has to be consumed immediately. Fuzzy Decision Tree is applied to predict the annual electricity requirement in India. Population and Per Capital gross domestic product (GDP) are taken as input variables and the electricity consumption is predict...

Most decision tree induction methods used for extracting knowledge in classification problems are unable to deal with uncertainties embedded within the data, associated with human thinking and perception. This paper describes the development of a novel tree induction algorithm which improves the classification accuracy of decision tree induction in non-deterministic domains. The research involv...