Fuzzy rule based cluster analysis to segment consumers’ preferences to eco and non-eco friendly products

2021 ◽  
Vol 24 (4) ◽  
pp. 343-351
Author(s):  
Sanjukta Ghosh ◽  
Doan Van Thang ◽  
Suresh Chandra Satapathy ◽  
Sachi Nandan Mohanty
Keyword(s):  
Fuzzy Rule ◽  
Environmental Knowledge ◽  
Purchase Behavior ◽  
Health Improvement ◽  
Rule Base ◽  
Environmental Attitude ◽  
Rule Based ◽  
Product Categories ◽  
Central Pollution Control Board ◽  
Develop Strategy

Environment protection and basic health improvement of all social communities is now considered as one of the key parameters for the development. It has become a responsibility for both industry and academia to optimize the usage of finite natural resources and preserve them. Efficient promotion and strategic marketing of Eco Friendly products can contribute to this development. It is important to consider any market as a heterogeneous mix, which requires well-organized and intelligent split or segmentation. A survey was conducted in Kolkata, metropolitan city in India, through a structured questionnaire to measure Perceived Environmental Knowledge, Perceived Environmental Attitude and Green Purchase Behavior associated to 18 product categories identified by Central Pollution Control Board for Eco Mark Scheme, 2002. Two hundred and twenty three data inputs from the respondents were analysed for this study. Here in this study a fuzzy rule based clustering technique was performed to segregate customers into two sections considering three parameters like Perceived Environmental Knowledge, Perceived Environmental Attitude and Green Purchase Behavior associated to Eco friendly product, which acts as an input variable. The rule base has linguistic variables like Significantly High, Little High, Medium, Little Low and Significantly Low and output as “Eco friendly” or “Non-ecofriendly” consumers. A set of 5×5×5= 125 rules were developed for output determination. They were designed manually and the method is applied for detection of a set of good rules. Thirteen such good rules were identified through Fuzzy Reasoning Tool, which can lead to better Decision Making and facilitate the marketers to develop strategy and take up effective marketing decisions.

Fuzzy Rule Interpolation

2011 ◽  
pp. 728-733 ◽  
Author(s):  
Szilveszter Kovács
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Fuzzy Reasoning ◽  
Fuzzy Relation ◽  
Rule Base ◽  
Rule Based ◽  
Fuzzy Rule Base ◽  
Current Observation ◽  
Rule Bases ◽  
Takagi Sugeno

The “fuzzy dot” (or fuzzy relation) representation of fuzzy rules in fuzzy rule based systems, in case of classical fuzzy reasoning methods (e.g. the Zadeh-Mamdani- Larsen Compositional Rule of Inference (CRI) (Zadeh, 1973) (Mamdani, 1975) (Larsen, 1980) or the Takagi - Sugeno fuzzy inference (Sugeno, 1985) (Takagi & Sugeno, 1985)), are assuming the completeness of the fuzzy rule base. If there are some rules missing i.e. the rule base is “sparse”, observations may exist which hit no rule in the rule base and therefore no conclusion can be obtained. One way of handling the “fuzzy dot” knowledge representation in case of sparse fuzzy rule bases is the application of the Fuzzy Rule Interpolation (FRI) methods, where the derivable rules are deliberately missing. Since FRI methods can provide reasonable (interpolated) conclusions even if none of the existing rules fires under the current observation. From the beginning of 1990s numerous FRI methods have been proposed. The main goal of this article is to give a brief but comprehensive introduction to the existing FRI methods.

scholarly journals A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 609 ◽  
Author(s):  
Marina Bardamova ◽  
Anton Konev ◽  
Ilya Hodashinsky ◽  
Alexander Shelupanov
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Rules ◽  
Rule Base ◽  
Rule Based ◽  
Gravitational Search ◽  
Rule Based Classifier

This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.

scholarly journals Green Purchase Behavior: The Role of Religiosity, Environmental Attitude, and Environmental Knowledge

2017 ◽  
Vol 2 (3) ◽  
pp. 83-88
Author(s):  
Julina Julina ◽  
Dwi Kartini ◽  
Popy Rufaidah ◽  
Martha Fani Cahyandito
Keyword(s):  
Structural Equation ◽  
Environmental Attitudes ◽  
Study Data ◽  
Jel Classification ◽  
Environmental Knowledge ◽  
Purchase Behavior ◽  
Equation Modeling ◽  
Environmental Attitude ◽  
Provincial Capital ◽  
Knowledge And Attitude

Objective - This study attempts to determine the effect of religiosity, environmental attitudes, and environmental knowledge towards green purchase behavior. Methodology/Technique - Data were collected by distributing questionnaire to 14 shopping centers in Pekanbaru City during April - September 2016. Pekanbaru city is one of the provincial capital in Indonesia which experienced many environmental problems. A total of 421 eligible respondents participated in this study. Data were analyzed using structural equation modeling. Findings – The results found that the effect of religiosity, environmental knowledge, and attitude toward green purchase behavior are significant. These three variables explain the green purchase behavior at 67.6%. Besides it also found that religiosity and environmental knowledge have the positive and meaningful impact on environmental attitudes. Therefore, it can be concluded that these two variables affect the green purchase behavior through environmental attitudes. Novelty - The model built in this study tried to integrate the spiritual aspect that has not been touched by previous researchers. The results of this study open up opportunities for further research to further improve both aspects of modeling in combination with other variables as well as the use of statistical analysis Type of Paper - Empirical Keywords: Religiosity; Environmental Attitude; Environmental Knowledge; Green Purchase Behavior. JEL Classification: I21, Q56, Q57.

Foreign Body Shape Classification Using Intuitionistic Fuzzy Rule Based Approach on Pediatric Radiography Images

2020 ◽  
Vol 10 (2) ◽  
pp. 45-57
Author(s):  
Vasumathy M. ◽  
Mythili T.
Keyword(s):  
Foreign Body ◽  
Uncertain Data ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Shape Classification ◽  
Rule Based ◽  
Intuitionistic Fuzzy ◽  
Precise Knowledge ◽  
Pediatric Radiography ◽  
Rule Based Approach

Segmentation is the significant key stage in image analysis towards partitioning an image into different regions which have homogeneous features such as color, shape, and texture which is very important in classifying different region shapes in an image. In general, images are considered fuzzy due to the uncertainty present in terms of vagueness. The regions contain imprecise gray levels and uncertain data values which makes the task of defining the membership function difficult due to lack of precise knowledge. The intuitionistic fuzzy rule-based shape classification approach is used to classify the different shapes, such as circular, polygon, sharp, and irregular of the aspired foreign body on pediatric radiography images. Experimental results show the effectiveness of the proposed method in contrast to conventional fuzzy rule base algorithm.

HANDLING HIGHLY-DIMENSIONAL CLASSIFICATION TASKS WITH HIERARCHICAL GENETIC FUZZY RULE-BASED CLASSIFIERS

2012 ◽  
Vol 20 (supp02) ◽  
pp. 73-104 ◽  
Author(s):  
DIMITRIS G. STAVRAKOUDIS ◽  
JOHN B. THEOCHARIS
Keyword(s):  
Fuzzy Rule ◽  
Classification Systems ◽  
Rule Base ◽  
Rule Based ◽  
Fuzzy Partition ◽  
High Performing ◽  
Dimensional Classification ◽  
Modern Classification ◽  
Classification Tasks ◽  
Rule Based Classifier

Many modern classification tasks are defined in highly-dimensional feature spaces. The derivation of high-performing genetic fuzzy rule-based classification systems (GFRBCSs) in such scenarios is a non-trivial task. This paper presents a framework for increasing the performance of GFRBCSs by creating a hierarchical fuzzy rule-based classifier. The proposed system is constructed through repeated invocations to a base GFRBCS procedure, considering at each step an input space fuzzy partition of a certain granularity. The best performing rules are inserted in the hierarchical rule base and the process is repeated again, considering a thicker granularity. The employed boosting scheme guides the algorithm in creating new rules to treat uncovered or misclassified patterns, thus monotonically increasing the performance of the classifier. Extensive experimental analysis in a number of real-world high-dimensional classification tasks proves the effectiveness of the proposed approach in increasing the performance of the base classifier, maintaining its interpretability to a considerable degree.

scholarly journals The Antecedents of Green Car Purchase Intention among Malaysian Consumers

2019 ◽  
Vol 4 (2) ◽  
Author(s):  
Yi Jin Lim ◽  
Selvan Perumal ◽  
Norzieiriani Ahmad
Keyword(s):  
Social Influence ◽  
Purchase Intention ◽  
Economic Incentives ◽  
Environmental Knowledge ◽  
Purchase Behavior ◽  
Kuala Lumpur ◽  
Environmental Attitude ◽  
Second Stage ◽  
Sampling Process ◽  
Multi Stage

This study aims to empirically examine the relationships among social influence, green labeling, economic incentives, environmental attitude, environmental knowledge, past green purchase behavior and green car purchase intention among the Malaysian consumers. A multi-stage sampling process with proportionate stratified sampling in the first stage and systematic mall intercept method in the second stage was applied in this study. Thereafter, a questionnaire survey was done among consumers aged 18 and above that visiting car dealers, namely Honda, Toyota and Nissan from the two representative states of Malaysia namely Penang and Kuala Lumpur. 417 out of 500 questionnaires distributed were returned back for data analysis using SmartPLS v.3 software. The results show that green labeling, economic incentives, environmental attitude are direct antecedents of green car purchase intention in Malaysia. However, social influence, environmental knowledge and past green purchase behavior do not have any influence on green car purchase intention in Malaysia. Lastly, implications of this study and limitations found in this study are discussed.

scholarly journals Construction and Optimization of Fuzzy Rule-Based Classifier with a Swarm Intelligent Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Li Mao ◽  
Qidong Chen ◽  
Jun Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
Fuzzy Classification ◽  
Rule Base ◽  
Classification Problems ◽  
Fuzzy Classifier ◽  
Swarm Optimization ◽  
Rule Based

In this paper, we propose a particle swarm optimization method incorporating quantum qubit operation to construct and optimize fuzzy rule-based classifiers. The proposed algorithm, denoted as QiQPSO, is inspired by the quantum computing principles. It employs quantum rotation gates to update the probability of each qubit with the corresponding quantum angle updating according to the update equation of the quantum-behaved particle swarm optimization (QPSO). After description of the principle of QiQPSO, we show how to apply QiQPSO to establish a fuzzy classifier through two procedures. The QiQPSO algorithm is first used to construct the initial fuzzy classification system based on the sample data and the grid method of partitioning the feature space, and then the fuzzy rule base of the initial fuzzy classifier is optimized further by QiQPSO in order to reduce the number of the fuzzy rules and thus improve its interpretability. In order to verify the effectiveness of the proposed method, QiQPSO is tested on various real-world classification problems. The experimental results show that the QiQPSO is able to effectively select feature variables and fuzzy rules of the fuzzy classifiers with high classification accuracies. The performance comparison with other methods also shows that the fuzzy classifier optimized by QiQPSO has higher interpretability as well as comparable or even better classification accuracies.

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