Performance Analysis of Classification Methods and Alternative Linear Programming Integrated with Fuzzy Delphi Feature Selection

Izadi, Bahram; Ranjbarian, Bahram; Ketabi, Saeed; Nassiri-Mofakham, Faria

doi:10.5815/ijitcs.2013.10.02

Cited by 8 publications

(10 citation statements)

References 24 publications

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“…After this stage, the study has employed FAHP for calculating the criteria weight. For the general understanding of expert's opinions about fuzziness, FDM can help to take the decisions of the group (Izadi, Ranjbarian, Ketabi, & Nassiri-Mofakham, 2013). In the first part of this study, researchers have examined the previous studies and literature of QM CSFs as an objective of study.…”

Section: Methodsmentioning

confidence: 99%

A Combined Hybrid Fuzzy Multiple Criteria Decision-making Approach to Evaluating of QM Critical Success Factors in SME's Hotels Firms

Mardani

Jusoh

Bagheri

et al. 2015

Procedia - Social and Behavioral Sciences

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The purpose of this study is to evaluate the importance of QM CSFs in Iranian SME's hotels firms. Primary criteria to evaluate QM CSFs are achieved by the literature survey. Through the FDM by industries and academia experts the importance CSFs were categorized in three main factors and 16 sub-factors. Two fuzzy MCDM methods are applied; fuzzy AHP and TOPSIS. The finding of this study indicated that human factors had the first rank from three perspectives and leadership as a sub-factor was the first rank from 16 sub-factors.

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Section: Methodsmentioning

confidence: 99%

A Combined Hybrid Fuzzy Multiple Criteria Decision-making Approach to Evaluating of QM Critical Success Factors in SME's Hotels Firms

Mardani

Jusoh

Bagheri

et al. 2015

Procedia - Social and Behavioral Sciences

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“…Sınıflandırma yöntemlerinin performanslarını karşılaştırmak için çapraz geçerlilik (cross validation) teknikleri kullanılmaktadır. Literatürde en çok kullanılan çapraz geçerlilik teknikleri; doğrulama örneği çapraz geçerlilik (test-holdout sample cross validation), bir birimi dışarıda tutma çapraz geçerlilik (leave-one-out cross validation), bazı birimleri dışarıda tutma çapraz geçerlilik (leave-some-out cross validation) ve kat çapraz geçerlilik ( fold cross validation) yaklaşımlarıdır [50,51] hızlandırma katsayıları ve doğrusal azalan atalet ağırlık stratejisi ise PSO uygulamalarında en çok kullanılan parametrelerdir [49].…”

Section: Siniflandirma Modelleri̇ni̇n Karşilaştirilmasi (Comparison Of unclassified

“…Başlangıçta veri setinden çıkartılan küme içindeki birimler 1 kümedeki birimlerden elde edilen ayırma fonksiyonu yardımıyla gruplara sınıflandırılırlar. Bu işlem her bir küme için yani defa tekrarlanır [50,53]. Her kümeden elde edilen doğru sınıflandırma sayılarının ortalaması ilgili yöntem için kat çapraz geçerlilik doğru sınıflandırma performansı olarak alınır.…”

Section: Siniflandirma Modelleri̇ni̇n Karşilaştirilmasi (Comparison Of the Classification Models)unclassified

Çok gruplu sınıflandırma problemlerine regresyon analizi ve matematiksel programlama tabanlı yeni bir yaklaşım

Doğan¹,

Orman²,

Örkcü³

et al. 2019

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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 A new approach for the multi group classification problems  Using the regression analysis for the obtaining the classification scores  Using the mathematical programming for the classifying of the units Classification is the problem of identifying to which of a set of categories a new observation belongs, on the basis of a training set of data containing observations whose category membership is known. In this study, for solving multi-group classification problems, a new two-stage hybrid classification method based on regression analysis and mathematical programming has been developed. In the first step of the proposed method, the classification score of each unit is estimated with the help of the linear regression equation for each unit. In the second step, the classification of the units is performed by the mathematical programming model based on clustering analysis. Figure A. Flow chart of proposed method Purpose: In this study, for solving multi-group classification problems, a new method has been proposed based on regression analysis and mathematical programming classification method. The purpose of this study is handled the multi-group classification problem with the help of the superiority of regression analysis from the statistical theory and the flexibility of mathematical programming by a two-stage detailed examination idea. Theory and Methods: The proposed method combines the strengths of regression analysis and mathematical programming method. Results: From the 10 real data sets taken the well-known literature and simulation study results, it is observed that the proposed method outperforms the regression analysis, mathematical programming and artificial neural network based classification methods. Conclusion:With the proposed method, it is possible to achieve high correct classification success in the multiclass classification problems.

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“…In [22] authors proposed a data classification method multi-group discriminant linear programming (MDLP) for classification problems with Support Vector Machine (SVM), Neural Networks. Local optima problem addressed and shows the MDLP not eliminate this problem and solution is less complex.…”

Section: Related Workmentioning

confidence: 99%

Multi-Feature Segmentation and Cluster based Approach for Product Feature Categorization

Singh¹

2016

IJITCS

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At a recent time, the web has become a valuable source of online consumer review however as the number of reviews is growing in high speed. It is infeasible for user to read all reviews to make a valuable or satisfying decision because the same features, people can write it contrary words or phrases. To produce a useful summary of domain synonyms words and phrase, need to be a group into same feature group. We focus on feature-based opinion mining problem and this paper mainly studies feature based product categorization from the number of users-generated review available on the different website. First, a multi-feature segmentation method is proposed which segment multi-feature review sentences into the single feature unit. Second part of speech dictionary and context information is used to consider the irrelevant feature identification, sentiment words are used to identify the polarity of feature and finally an unsupervised clustering based product feature categorization method is proposed. Clustering is unsupervised machine learning approach that groups feature that have a high degree of similarity in a same cluster. The proposed approach provides satisfactory results and can achieve 100% average precision for clustering based product feature categorization task. This approach can be applicable to different product.

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Performance Analysis of Classification Methods and Alternative Linear Programming Integrated with Fuzzy Delphi Feature Selection

Cited by 8 publications

References 24 publications

A Combined Hybrid Fuzzy Multiple Criteria Decision-making Approach to Evaluating of QM Critical Success Factors in SME's Hotels Firms

A Combined Hybrid Fuzzy Multiple Criteria Decision-making Approach to Evaluating of QM Critical Success Factors in SME's Hotels Firms

Çok gruplu sınıflandırma problemlerine regresyon analizi ve matematiksel programlama tabanlı yeni bir yaklaşım

Multi-Feature Segmentation and Cluster based Approach for Product Feature Categorization

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