Particle swarm optimized multiple regression linear model for data classification

Satapathy, Suresh Chandra; Murthy, J. V. R.; Reddy, P. Jayarama; Misra, Bijan Bihari; Dash, P.K.; Panda, G. K.

doi:10.1016/j.asoc.2008.05.007

Cited by 26 publications

(19 citation statements)

References 9 publications

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“…In this study, for constants c 1 and c 2 , two options were considered. In the first option, c 1 and c 2 are both set at 2 and in the second option, c 1 and c 2 are both set at 1.45 [42,47,48].…”

Section: Implementation Of Pso To 3-p Weibull Parameter Estimation Anmentioning

confidence: 99%

Estimating the parameters of 3-p Weibull distribution using particle swarm optimization: A comprehensive experimental comparison

Örkçü

Özsoy

Aksoy

et al. 2015

Applied Mathematics and Computation

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Section: Implementation Of Pso To 3-p Weibull Parameter Estimation Anmentioning

confidence: 99%

Estimating the parameters of 3-p Weibull distribution using particle swarm optimization: A comprehensive experimental comparison

Örkçü

Özsoy

Aksoy

et al. 2015

Applied Mathematics and Computation

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“…Bu bölümde, Satapathy vd. [37] ve Lam ve Moy [38] modellerine dayalı olarak önerilen iki aşamalı sınıflandırma yaklaşımı yer almaktadır. Önerilen yaklaşımın ilk aşamasında her bir birimin sınıflandırma skoru Satapathy vd.…”

Section: öNeri̇len Siniflandirma Modeli̇ (The Proposed Classification Munclassified

“…Önerilen yaklaşımın ilk aşamasında her bir birimin sınıflandırma skoru Satapathy vd. [37]'e benzer bir şekilde her birim için oluşturulan doğrusal regresyon denklemi yardımıyla tahmin edilmektedir. İkinci aşamada ise Lam ve Moy [38] sonra grupları birbirinden ayıran ve birimlerin gruplarını tayin eden eşik değerlerinin bulunması model (10)' un çözülmesiyle elde edilmiş ve eşik değerleri sırasıyla 1,5 ve 2,7 olarak elde edilmiştir.…”

Section: öNeri̇len Siniflandirma Modeli̇ (The Proposed Classification Munclassified

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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“…The model based on multiple regressions is known as multiple linear regression (MLR) [14]. MLR attempts to model the relationship between two or more explanatory variables and a response variable by fitting linear equation to observed data.…”

Section: A Multiple Linear Regressionmentioning

confidence: 99%

Recognition of the state of mind using Hamming Swarm Net

Swain

Kamila

Misra³

2012

2012 World Congress on Information and Communication Technologies

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Recognition of the state of mind has been attempted by many researchers using different computational techniques, but the achievement is not quite significant. In this paper, "whether a person can cause harm to others or not" has been considered as one of the states of mind of human being to be recognized. This paper presents Hamming Swarm Net (HSN) which is a hybridization the Hamming Net (HN) and the Particle Swarm Optimization (PSO) techniques. HN being a competitive network is usually used tasks like clustering, but in this work it is employed as a tool for pattern classification. HN considers fixed exemplar vectors, which restricts its mapping efficiency. The proposed technique evolves the optimal set of exemplar vectors by exercising intelligence of swarm and significantly improves the recognition capabilities. The moral database of UCI machine learning repository is taken here for recognition one of the states of mind. Simulation study shows that recognition capabilities of HSN is superior in comparisonto many other techniques such as Multi Layer Perceptron (MLP), Functional Link Artificial Neural Network (FLANN), Polynomial Neural Network (PNN), Multiple Linear Regression (MLR), and Hamming Network (HN).

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Particle swarm optimized multiple regression linear model for data classification

Cited by 26 publications

References 9 publications

Estimating the parameters of 3-p Weibull distribution using particle swarm optimization: A comprehensive experimental comparison

Estimating the parameters of 3-p Weibull distribution using particle swarm optimization: A comprehensive experimental comparison

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

Recognition of the state of mind using Hamming Swarm Net

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