Automatic generation of fuzzy classification rules using granulation-based adaptive clustering

A central problem of fuzzy modelling is the generation of fuzzy rules that fit the data to the highest possible extent. In this study, we present a method for automatic generation of fuzzy rules from data. The main advantage of the proposed method is its ability to perform data clustering without the requirement of predefining any parameters including number of clusters. The proposed method creates data clusters at different levels of granulation and selects the best clustering results based on some measures. The proposed method involves merging clusters into new clusters that have a coarser granulation. To evaluate performance of the proposed method, three different datasets are used to compare performance of the proposed method to other classifiers: SVM classifier, FCM fuzzy classifier, subtractive clustering fuzzy classifier. Results show that the proposed method has better classification results than other classifiers for all the datasets used

An artificial immune system for fuzzy-rule induction in data mining

This work proposes a classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems. The algorithm consists of two parts: a sequential covering procedure and a rule evolution procedure. Each antibody (candidate solution) corresponds to a classification rule. The classification of new examples (antigens) considers not only the fitness of a fuzzy rule based on the entire training set, but also the affinity between the rule and the new example. This affinity must be greater than a threshold in order for the fuzzy rule to be activated, and it is proposed an adaptive procedure for computing this threshold for each rule. This paper reports results for the proposed algorithm in several data sets. Results are analyzed with respect to both predictive accuracy and rule set simplicity, and are compared with C4.5rules, a very popular data mining algorithm

ART/SOFM: A Hybrid Approach to the TSP

We present a new method of solving large scale travelling salesman problem (TSP) instances using a combination of adaptive resonance theory (ART) and self organizing feature maps (SOFM). We divide our algorithm into three phases: phase one uses ART to form clusters of cities; phase two uses a novel modification of the traditional SOFM algorithm to solve a slight variant of the TSP in each cluster of cities; and phase three uses another version of the SOFM to link all the clusters. The experimental results show that our algorithm finds approximate solutions which are about 13% longer than those reported by the chained Lin Kernighan method for problem sizes of 14,000 citie

A Hybrid Fuzzy Time Series Technique for Forecasting Univariate Data

In this paper a hybrid forecasting technique that integrates Cat Swarm optimization Clustering (CSO-C) and Particle Swarm Optimization (PSO) with Fuzzy Time Series (FTS) forecasting is presented. In the three stages of FTS, CSO-C found application at the fuzzification module where its efficient capability in terms of data classification was utilized to neutrally divide the universe of discourse into unequal parts. Then, disambiguated fuzzy relationships were obtained using Fuzzy Set Group (FSG). In the final stage, PSO was adopted for optimization; by tuning weights assigned to fuzzy sets in a rule. This rule is a fuzzy logical relationship induced from FSG. The forecasting results showed that the proposed method outperformed other existing methods; using RMSE and MAPE as performance metrics.            

A Brief Review of Cuckoo Search Algorithm (CSA) Research Progression from 2010 to 2013

Cuckoo Search Algorithm is a new swarm intelligence algorithm which based on breeding behavior of the Cuckoo bird. This paper gives a brief insight of the advancement of the Cuckoo Search Algorithm from 2010 to 2013. The first half of this paper presents the publication trend of Cuckoo Search Algorithm. The remaining of this paper briefly explains the contribution of the individual publication related to Cuckoo Search Algorithm. It is believed that this paper will greatly benefit the reader who needs a bird-eyes view of the Cuckoo Search Algorithm’s publications trend

MODIFIED SWEEP ALGORITHM FOR ROUTE SELECTION IN PUBLIC BUS ROUTING PROBLEM USING FUZZY DATA

This paper investigates public bus route selection where demand is uncertain and evaluates the role of fuzzy logic in the MSA. The uncertain demand data are presented in linguistic form and transformed into fuzzy numbers. The crisp values obtained by the fuzzy logic are used to replace the exact demand in selecting best routes. The patterns of fuzzy data are presented to show capability of fuzzy data in representing exact data