This paper presents a new fuzzy multicriteria classi®cation method, called PROAFTN, for assigning alternatives to prede®ned categories. This method belongs to the class of supervised learning algorithms and enables to determine the fuzzy indi€erence relations by generalising the indices (concordance and discordance) used in the ELECTRE III method. Then, it assigns the fuzzy belonging degree of ...

Conventionally modelling and simulation of complex nonlinear systems has been to construct a mathematical model and examine the system’s evolution or its control. This kind of approach can fail for many of the very large non-linear and complex systems being currently studied. With the invention of new advanced high-speed computers and the application of artificial intelligence paradigms new tec...

The research area of Data Mining or Knowledge Discovery in Databases has emerged in response to the challenges of analyzing the tremendously growing datasets gathered nowadays by companies and research institutions. Classification is one important task of data mining, where fuzzy techniques to extract classification rules from data are appealing due to their human understandable modeling. Often...

Wikipedia is one of the largest growing structured resources on the Web and can be used as a training corpus in natural language processing applications. In this work, we present a method to categorize named entities under the hierarchical fine-grained categories provided by the Wikipedia taxonomy. Such a categorization can be further used to extract semantic relations among these named entitie...

This paper presents a supervised training algorithm that implements fuzzy reasoning on a spiking neural network. Neuron selectivity is facilitated using receptive fields that enable individual neurons to be responsive to certain spike train frequencies and behave in a similar manner as fuzzy membership functions. The connectivity of the hidden and output layers in the fuzzy spiking neural netwo...

This paper presents a supervised training algorithm that implements fuzzy reasoning on a spiking neural network. Neuron selectivity is facilitated using receptive fields that enable individual neurons to be responsive to certain spike train firing rates and behave in a similar manner as fuzzy membership functions. The connectivity of the hidden and output layers in the fuzzy spiking neural netw...

A new technique named as Intuitionistic fuzzy positive deviation method for decision making is the selection of the professional students based on their skills by the recruiters using Intuitionistic Fuzzy sets. Sanchez's approach for decision making is studied and the concept is generalized by the application of Intuitionistic Fuzzy Set (IFS) theory. Through a survey the relations between ...

One of the most known and effective methods in supervised classification is the K-Nearest Neighbors classifier. Several approaches have been proposed to enhance its precision, with the Fuzzy K-Nearest Neighbors (Fuzzy-kNN) classifier being among the most successful ones. However, despite its good behavior, Fuzzy-kNN lacks of a method for properly defining several mechanisms regarding the repres...

The elicitation method of the fuzzy membership function depends on the interpretation of the membership function. This paper applies a recently developed neural network framework, Plausible Neural Network (PNN), to generate fuzzy membership functions automatically with or without class labeling based on the similarity and likelihood measurement. The approach combining supervised and unsupervise...