In recent years, imperialist competitive algorithm (ICA), genetic algorithm (GA), and hybrid fuzzy classification systems have been successfully and effectively employed for classification tasks of data mining. Due to overcoming the gaps related to ineffectiveness of current algorithms for analysing high-dimension independent datasets, a new hybrid approach, named HYEI, is presented to discover generic rule-based systems in this paper. This proposed approach consists of three stages and combines an evolutionary-based fuzzy system with two ICA procedures to generate high-quality fuzzy-classification rules. Initially, the best feature subset is selected by using the embedded ICA feature selection, and then these features are used to generate basic fuzzy-classification rules. Finally, all rules are optimized by using an ICA algorithm to reduce their length or to eliminate some of them. The performance of HYEI has been evaluated by using several benchmark datasets from the UCI machine learning repository. The classification accuracy attained by the proposed algorithm has the highest classification accuracy in 6 out of the 7 dataset problems and is comparative to the classification accuracy of the 5 other test problems, as compared to the best results previously published.

D. Jalal Nouri

M. Saniee Abadeh

F. Ghareh Mohammadi

HYEI: A New Hybrid Evolutionary Imperialist Competitive Algorithm for Fuzzy Knowledge Discovery

In real life, information available on situations/issues/problems is vague, inexact, or insufficient and so the parameters involved therein are grasped in an uncertain way by the decision maker. But in real life such uncertainty is unavoidable. One possible way out is to consider the knowledge of experts about the parameters involved as fuzzy data. In a network, the arc length may represent time or cost. In Relevant literature reports there are several methods to solve such problems in network-flow. This paper proposes an optimized path for use in networks, using trapezoidal intuitionistic fuzzy numbers, assigned to each arc length in a fuzzy environment. It proposes a new algorithm to find the optimized path and implied distance from source node to destination node.

P. Jayagowri

G. Geetha Ramani

Using Trapezoidal Intuitionistic Fuzzy Number to Find Optimized Path in a Network

We introduce the notion of (i-v) semiprime (irreducible) fuzzy ideals of semigroups and investigate its different algebraic properties. We study the interrelation among (i-v) prime fuzzy ideals, (i-v) semiprime fuzzy ideals, and (i-v) irreducible fuzzy ideals and characterize regular semigroups by using these (i-v) fuzzy ideals.

Sukhendu Kar

Paltu Sarkar

Kostaq Hila

Interval-Valued Semiprime Fuzzy Ideals of Semigroups

We introduce and develop the initial theory of vague soft hyperalgebra by introducing the novel concept of vague soft hypergroups, vague soft subhypergroups, and vague soft hypergroup homomorphism. The properties and structural characteristics of these concepts are also studied and discussed.

Ganeshsree Selvachandran

Abdul Razak Salleh

Vague Soft Hypergroups and Vague Soft Hypergroup Homomorphism

Today green supply chain is considered all around the world and supplier selection has been changed regarding these green and carbon emission criteria, so green supplier selection has been a major problem in this area. In this study we use fuzzy time function to assist managers in green supplier selection under uncertainty and ambiguity. This function will consider derivation from the goal during the time and by using it, and we will be able to have the best supplier in every period after having some modification in legal limitations for green supplier selection criteria. We use a fuzzy TOPSIS to have better initial weighting in TODIM, a discrete multicriteria method based on prospect theory in uncertainty (known as TODIM in Portuguese) decision making method. The results indicated that our proposed approach can easily and effectively accommodate criteria with gains and loss functions during time and also by using this method we will have a more reasonable predict of our suppliers ranking in future and that will help us in future investment in these suppliers. Finally it has been shown in car industries in Iran.

Alireza Arshadi Khamseh

Mahdi Mahmoodi

A New Fuzzy TOPSIS-TODIM Hybrid Method for Green Supplier Selection Using Fuzzy Time Function

Ferdinando Di Martino

Irina Perfilieva

Salvatore Sessa

Sabrina Senatore

Fuzzy Methods and Approximate Reasoning in Geographical Information Systems

Buckley&#x2019;s approach (Buckley (2004), (2005), (2006)) uses sets of confidence intervals by taking into consideration both of the uncertainty and impreciseness of concepts that produce triangular shaped fuzzy numbers for the estimator. This approach produces fuzzy test statistics and fuzzy critical values in hypothesis testing. In addition, the sample size is fixed for this test. When data comes sequentially, however, it is not suitable to study with a fixed sample size test. In such cases, sequential and group sequential tests are recommended. Unlike a sequential test, a group of sequential test provides substantial savings in sample and enables us to make decisions as early as possible. This intends paper to combine the benefits of group sequential test and Buckley's approach using <svg     style="vertical-align:-0.1638pt;width:9.7749996px;" id="M1" height="7.9499998" version="1.1" viewBox="0 0 9.7749996 7.9499998" width="9.7749996"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
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</svg>-cuts can be used within the group sequential tests. To illustrate the test more explicitly a numerical example is also given.

Duygu &#x130;&#xE7;en

Sevil Bacanl&#x131;

S&#xfc;leyman G&#xfc;nay

Fuzzy Approach for Group Sequential Test

This paper presents an extended analytical approach to evaluating continuous,
monotonic functions of independent fuzzy numbers. The approach is
based on a parametric <i>&#x3B1;</i>-cut representation of fuzzy numbers and allows for
the inclusion of parameter uncertainties into mathematical models.

Arthur Seibel

Josef Schlattmann

An Extended Analytical Approach to Evaluating Monotonic Functions of Fuzzy Numbers

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<p><em>Advances in Fuzzy Systems</em> provides an international forum for original research articles in the theory and applications of fuzzy subsets and systems.</p>


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<p>Chief Editor, Professor Melin, is a professor at the Tijuana Institute of Technology. Her research interests include modular neural networks, type-2 fuzzy logic, pattern recognition, fuzzy control, neuro-fuzzy and genetic-fuzzy hybrid approaches.</p>


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Advances in Fuzzy Systems - Table of contents 2014

title

Advances in Fuzzy Systems provides an international forum for original research articles in the theory and applications of fuzzy subsets and systems.

Advances in Fuzzy Systems