This research proposes a new model for constructing decision trees using intervalvalued fuzzy membership values. Most existing fuzzy decision trees do not consider the uncertainty associated with their membership values, however, precise values of fuzzy membership values are not always possible. In this paper, we represent fuzzy membership values as intervals to model uncertainty and employ the...

In this paper, we propose a new feature selection method called class dependency based feature selection for dimensionality reduction of the macular disease dataset from pattern electroretinography (PERG) signals. In order to diagnosis of macular disease, we have used class dependency based feature selection as feature selection process, fuzzy weighted pre-processing as weighted process and dec...

This paper presents a new decision tree learning algorithm, fuzzy min-max decision tree (FMMDT) based on fuzzy min-max neural networks. In contrast with traditional decision trees in which a single attribute is selected as the splitting test, the internal nodes of the proposed algorithm contain a fuzzy min-max neural network. In the proposed learning algorithm, the exibility inherent in the fuz...

In context-aware systems, one of the main challenges is how to tackle context uncertainty well, since perceived context always yields uncertainty and ambiguity with consequential effect on the performance of context-aware systems. We argue that uncertainty is mainly generated by two sources. One is sensor’s inherent inaccuracy and unreliability. The other source is deduction process from low-le...

An inverse minimum spanning tree problem is to make the least modification on the edge weights such that a predetermined spanning tree is a minimum spanning tree with respect to the new edge weights. In this paper, a type of fuzzy inverse minimum spanning tree problem is introduced from a LAN reconstruction problem, where the weights of edges are assumed to be fuzzy variables. The concept of fu...

Data-based identification of fuzzy rule-based classifiers for high-dimensional problems is addressed. A crisp binary decision tree approach is proposed for the selection of the relevant features and effective initial partitioning of the input domain. The decision tree is then transformed into a fuzzy rule-based classifier. Fuzzy classifiers have more flexible decision boundaries than decision t...

As the number of networked computers grows, intrusion detection becomes an essential component in keeping networks secure. Various approaches for intrusion detection are currently being in use with each one has its own merits and demerits. This paper presents the work to test and improve the performance of an intrusion detection system based on C-fuzzy decision tree, a new class of decision tre...

Feature subset selection supports the classification task by reducing the search space as well as by removing irrelevant and random features, which might compromise the resulting classification model. Decision trees perform an embedded feature selection as they select only the relevant features for the splitting of the datasets during the induction process. FUZZYDT is a fuzzy decision tree whic...

The risk response development is one of the main phases in the project risk management that has major impacts on a large-scale project’s success. Since projects are unique, and risks are dynamic through the life of the projects, it is necessary to formulate responses of the important risks. Conventional approaches tend to be less effective in dealing with the imprecise of the risk response deve...