Case-based reasoning for classification in the mixed data sets employing the compound distance methods

Rezvan, Mohammad Taghi; Hamadani, Ali Zeinal; Shalbafzadeh, Ali

doi:10.1016/j.engappai.2013.07.014

Cited by 32 publications

(13 citation statements)

References 41 publications

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“…In this research, test case (TC) and retrieved case (RC) are represented as two unstructured set of feature-value pairs (Rezvan et al, 2013), which involve problem feature-value vectors (denoted as TPV and RPV) and solution feature-value vectors (denoted as TSV and RSV). Fig.…”

Section: Frameworkmentioning

confidence: 99%

Incorporating adaptability-related knowledge into support vector machine for case-based design adaptation

Qi¹,

Hu²,

Peng³

2015

Engineering Applications of Artificial Intelligence

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Section: Frameworkmentioning

confidence: 99%

Incorporating adaptability-related knowledge into support vector machine for case-based design adaptation

Qi¹,

Hu²,

Peng³

2015

Engineering Applications of Artificial Intelligence

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“…분류 알고리즘들을 그룹화한 연구는 주로 분류 알고리즘들 을 비교․분석하는 논문에서 제시되고 있다 (Lim et al, 2000;Ng and Jordan, 2002;Kotsiantis, 2007;Rubinstein et al, 1997;Hung et al, 2006 (Boriah et al, 2008;Chen, et al, 2009;Hsu et al, 2011;Lesot et al, 2008;Rezvan et al, 2013;Esposito et al, 2000;Brodley et al, 1995).…”

Section: 분류 알고리즘들의 그룹화 및 특성unclassified

“…둘째, k-NN에서 범주형 변수를 다루는 기법 이다. k-NN 기법에서는 주어진 케이스(case)와 표본 내 다른 케이스간의 거리 계산이 핵심 요소이므로 범주형 변수간 거리 계산을 위한 척도에 대한 다양한 연구가 수행되어 왔다 (Boriah et al, 2008;Chen et al, 2009;Lesot et al, 2009;Rezvan et al, 2013).…”

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Hybrid Classifiers of Classification Techniques for Mixed Data

Hong¹,

Park²,

Park³

2017

JKIIE

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Mixed data with numeric variables and categorical variables are appearing in many areas such as credit scoring, medical diagnosis and manufacturing products. Most Classification techniques are suitable for each variable type of mixed data. For example, techniques using Euclidean distance are suitable for numerical variables and another techniques using symbolic logic are suitable for categorical variables. In this paper, we propose a hybrid method of classifiers to improve performance of the classification algorithm. Main idea is to deal with the categorical and numerical attributes separately with appropriate techniques. First, a whole data is partitioned into several subsets by applying decision tree only to categorical variables. Next, posterior probability is obtained by applying either k-NN or SVM to numerical variables in each leaf node of decision tree. Six data (Australian credit, German credit, Japan credit, Mammographic mass, churn, bank) of the UCI Machine Learning Repository are used to evaluate performance of the proposed hybrid classifier. Performance of a hybrid k-NN classifier is improved comparing with the k-NN. Performance of a hybrid SVM is slightly better than that of SVM. †

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“…Case representation must be based on the various existing knowledge representation, almost all existing knowledge representation can be used as case representation method [16]. For the same case, Cases can be expressed using different methods.…”

Section: Case Tepresentation Methodsmentioning

confidence: 99%