scholarly journals Computing with words with the use of inverse RDM models of membership functions

2015 ◽  
Vol 25 (3) ◽  
pp. 675-688 ◽  
Author(s):  
Andrzej Piegat ◽  
Marcin Pluciński
Keyword(s):  
Interval Arithmetic ◽  
Distance Measure ◽  
Specific Problem ◽  
Human Mind ◽  
Relative Distance ◽  
Fuzzy Membership ◽  
Computing With Words ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Flight Delay

Abstract Computing with words is a way to artificial, human-like thinking. The paper shows some new possibilities of solving difficult problems of computing with words which are offered by relative-distance-measure RDM models of fuzzy membership functions. Such models are based on RDM interval arithmetic. The way of calculation with words was shown using a specific problem of flight delay formulated by Lotfi Zadeh. The problem seems easy at first sight, but according to the authors’ knowledge it has not been solved yet. Results produced with the achieved solution were tested. The investigations also showed that computing with words sometimes offers possibilities of achieving better problem solutions than with the human mind.

Feature selection by a distance measure method of subnormal and non-convex fuzzy sets

2021 ◽  
pp. 1-7
Author(s):  
Letao Qu ◽  
Bohyun Wang ◽  
Joon S. Lim
Keyword(s):  
Feature Selection ◽  
Fuzzy Sets ◽  
Distance Measure ◽  
Critical Role ◽  
Classification Systems ◽  
Fuzzy Rules ◽  
Fuzzy Classification ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions

Distance measures of fuzzy sets have been developed for feature selection and finding redundant features in the fields of decision-making, prediction, and classification problems. Terms commonly used in the definition of fuzzy sets are normal and convex fuzzy sets. This paper extends the general fuzzy set definitions to subnormal and non-convex fuzzy sets that are more precise when implementing uncertain knowledge representations by weighing fuzzy membership functions. A distance measure method for subnormal and non-convex fuzzy sets is proposed for embedded feature selection. Constructing fuzzy membership functions and extracting fuzzy rules play a critical role in fuzzy classification systems. The weighted fuzzy membership functions prevent the combinatorial explosion of fuzzy rules in multiple fuzzy rule-based systems. The proposed method was validated by a comparison with two other methods. Our proposed method demonstrated higher accuracies in training and test, with scores of 97.95% and 93.98%, respectively, compared to the other two methods.

scholarly journals Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

2021 ◽  
Author(s):  
Jia-Bin Zhou ◽  
Yan-Qin Bai ◽  
Yan-Ru Guo ◽  
Hai-Xiang Lin
Keyword(s):  
Support Vector Machine ◽  
Negative Impact ◽  
Twin Support Vector Machine ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Intuitionistic Fuzzy ◽  
Benchmark Datasets ◽  
The Impact

AbstractIn general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

A method for automatic generation of fuzzy membership functions for mobile device’s characteristics based on Google Trends

2013 ◽  
Vol 29 (2) ◽  
pp. 510-517 ◽  
Author(s):  
Aitor Almeida ◽  
Pablo Orduña ◽  
Eduardo Castillejo ◽  
Diego López-de-Ipiña ◽  
Marcos Sacristán
Keyword(s):  
Automatic Generation ◽  
Google Trends ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions

Fuzzy membership functions of procurement selection criteria

2002 ◽  
Vol 20 (3) ◽  
pp. 285-296 ◽  
Author(s):  
S. Thomas Ng ◽  
Duc Thanh Luu ◽  
Swee Eng Chen ◽  
Ka Chi Lam
Keyword(s):  
Selection Criteria ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions

scholarly journals The Performance of Stock Portfolios formed using Fuzzy Logic

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Amit K. Sinha 1 ◽  
Andrew J. Jacob 2
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Expert System ◽  
Expert Systems ◽  
Fuzzy Membership ◽  
Thought Process ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Stock Portfolios ◽  
Financial Securities

Expert systems, a type of artificial intelligence that replicate how experts think, can aide unskilled users in making decisions or apply an expert’s thought process to a sample much larger than could be examined by a human expert. In this paper, an expert system that ranks financial securities using fuzzy membership functions is developed and applied to form portfolios. Our results indicate that this approach to form stock portfolios can result in superior returns than the market as measured by the return on the S&P 500. These portfolios may also provide superior risk-adjusted returns when compared to the market.

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