A new framework with similarity reasoning and monotone fuzzy rule relabeling for fuzzy inference systems

A complete and monotonically-ordered fuzzy rule base is necessary to maintain the monotonicity property of a Fuzzy Inference System (FIS). In this paper, a new monotone fuzzy rule relabeling technique to relabel a non-monotone fuzzy rule base provided by domain experts is proposed. Even though the Genetic Algorithm (GA)-based monotone fuzzy rule relabeling technique has been investigated in our previous work [7], the optimality of the approach could not be guaranteed. The new fuzzy rule relabeling technique adopts a simple brute force search, and it can produce an optimal result. We also formulate a new two-stage framework that encompasses a GA-based rule selection scheme, the optimization based-Similarity Reasoning (SR) scheme, and the proposed monotone fuzzy rule relabeling technique for preserving the monotonicity property of the FIS model. Applicability of the two-stage framework to a real world problem, i.e., failure mode and effect analysis, is further demonstrated. The results clearly demonstrate the usefulness of the proposed framework.

A new online updating framework for constructing monotonicity-preserving fuzzy inference systems

In this paper, a new online updating framework for constructing monotonicity-preserving Fuzzy Inference Systems (FISs) is proposed. The framework encompasses an optimization-based Similarity Reasoning (SR) scheme and a new monotone fuzzy rule relabeling technique. A complete and monotonically-ordered fuzzy rule base is necessary to maintain the monotonicity property of an FIS model. The proposed framework attempts to allow a monotonicity-preserving FIS model to be constructed when the fuzzy rules are incomplete and not monotonically-ordered. An online feature is introduced to allow the FIS model to be updated from time to time. We further investigate three useful measures, i.e., the belief, plausibility, and evidential mass measures, which are inspired from the Dempster- Shafer theory of evidence, to analyze the proposed framework and to give an insight for the inferred outcomes from the FIS model.

Interval-based and fuzzy set-based approaches to modeling of fuzzy inference systems with the local monotonicity property

Even though the importance of the local monotonicity property for function approximation problems is well established, there are relative few investigations addressing issues related to the fulfillment of the local monotonicity property in Fuzzy Inference System (FIS) modeling. We have previously conducted a preliminary study on the local monotonicity property of FIS models, with the assumption that the extrema point(s) (i.e., the maximum and/or minimum point(s)) is either known precisely or totally unknown. However, in some practical situations, the extrema point(s) can be known imprecisely (as an interval or a fuzzy set). In this paper, the imprecise information is exploited to construct an FIS model that fulfills the local monotonicity property. A procedure to estimate the extrema point(s) of a function is devised. Applicability of the findings to a datadriven modeling problem is further demonstrated.

Linguistic computational model based on 2-tuples and intervals

Herrera and Mart́inez initiated a 2-tuple fuzzy linguistic representation model for computing with words.Moreover, Wang and Hao further developed a new 2-tuple fuzzy linguistic representation model to deal with the linguistic term sets that are not uniformly and symmetrically distributed. This study proposes another linguistic computational model based on 2-tuples and intervals, which we call an interval version of the 2-tuple fuzzy linguistic representation model. The proposed model possesses three steps: 1) interval numerical scale; 2) computation based on interval numbers; and 3) a generalized inverse operation of the interval numerical scale. The first step transforms linguistic terms into interval numbers, based on which the second step is executed with output as an interval number. Finally, this number is then mapped into the interval of the linguistic 2-tuples by the generalized inverse operation. This study also generalizes the numerical scale approach, presented in the Wang and Hao model, to set the interval numerical scale, by considering the context where semantics of linguistic terms are defined by interval type-2 fuzzy sets (IT2 FSs). In order to compare the proposed model with the existing linguistic computational model based on IT2 FSs, we have conducted extensive simulations. The simulations demonstrate that the results obtained by our proposal are consistent with the results of the linguistic computational model based on IT2 FSs (in some sense) in a vast majority of cases.

Classification and optimization of product review information using soft computing models

A soft computing framework to classify and optimize text-based information extracted from customers' product reviews is proposed in this paper. The soft computing framework performs classification and optimization in two stages. Given a set of keywords extracted from unstructured text-based product reviews, a Support Vector Machine (SVM) is used to classify the reviews into two categories (positive and negative reviews) in the first stage. An ensemble of evolutionary algorithms is deployed to perform optimization in the second stage. Specifically, the Modified micro Genetic Algorithm (MmGA) optimizer is applied to maximize classification accuracy and minimize the number of keywords used in classification. Two Amazon product reviews databases are employed to evaluate the effectiveness of the SVM classifier and the ensemble of MmGA optimizers in classification and optimization of product related keywords. The results are analyzed and compared with those published in the literature. The outputs potentially serve as a list of impression words that contains useful information from the customers' viewpoints. These impression words can be further leveraged for product design and improvement activities in accordance with the Kansei engineering methodology.

The influence of protective and risk factors in individual, peer and school domains on Thai adolescents' alcohol and illicit drug use: a survey

This study investigates risk and protective factors for substance abuse in a sample of 1778 students attending technical colleges in Bangkok and Nakhon Ratchasima provinces of Thailand using a self-report questionnaire modified from the Communities That Care youth survey. Low school commitment was strongly associated with illicit drug use, with adjusted odds ratios ranging from 2.84 (glue sniffing) to 10.06 (ecstasy). Having friends using drugs, and friends with delinquent behaviors increased the risk of using alcohol and illegal drugs, with adjusted odds ratios of 6.84 and 6.72 respectively for marijuana use. For protective factors, approximately 40-60% of students with high levels of moral belief, participation in religious activities, and social skills were less likely to use alcohol. It is concluded that peer influence is a significant contributor to Thai adolescents' participation in substance abuse and that engaging in religiosity may assist adolescents to internalize negative aspects of harmful drugs into positive perceptions and encourage them to avoid alcohol and illegal drugs.