Symbolization assisted SVM classifier for noisy data

Kumar, Ritwik; Kulkarni, Abhijit J.; Jayaraman, Vaidyanathan; Kulkarni, Bhaskar D.

doi:10.1016/j.patrec.2003.12.012

Cited by 13 publications

(8 citation statements)

References 17 publications

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“…[36][37][38]. While some work on the influence of scaling and discretisation of continuous attributes [39][40][41] exists, the effect of coding of categorical attributes has to our best knowledge not been investigated.…”

Section: Support Vector Machinesmentioning

confidence: 99%

“…Obviously, the resulting dataset depends on the definition of the critical boundaries x c between two adjacent symbols. As an unfavourable choice of values may lead to a loss of meaningful information [40,41], the DPP choice of discretisation is not without risk. Popular variants of discretisation are analysed [18], confirming their relevance for classifier performance.…”

Section: Data Projectionmentioning

confidence: 99%

See 1 more Smart Citation

The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing

Crone

Lessmann

Stahlbock

2006

European Journal of Operational Research

185

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Section: Support Vector Machinesmentioning

confidence: 99%

Section: Data Projectionmentioning

confidence: 99%

The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing

Crone

Lessmann

Stahlbock

2006

European Journal of Operational Research

185

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“…Support vector machine (SVM) is a powerful state‐of‐the‐art data mining tool that uses machine learning theory to maximize predictive accuracy while automatically avoiding over‐fit to the data . SVM systems use the input data into a high dimensional feature space and subsequently carry out the linear regression in the feature space.…”

Section: Methodsmentioning

confidence: 99%

“…SVM systems use the input data into a high dimensional feature space and subsequently carry out the linear regression in the feature space. SVMs approximate the function in the following form :

f (x) = \sum_{i}^{n} φ (x_{i}) ω + b

where n is the total number of input–output pairs, φ( x ) is called as the feature map, x is the input space, f ( x ) is the output, and w and b are the coefficients. w and b are estimated by minimizing:

min_{w, b, ξ, ξ *} J (w, ξ, ξ^{*}, b) = \frac{1}{2} | | w | |^{2} + C \sum_{i} (ξ_{i} + ξ_{i}^{*})

subject to:

y_{i} - φ^{T} (x_{i}) w - b \leq ɛ + ξ_{i}

φ^{T} (x_{i}) w + b - y_{i} \leq ɛ + ξ_{i}^{*}

where C is a regularized constant determining the tradeoff between the training error and the model flatness, ε is a prescribed parameter of the ε‐insensitive loss function, ξ and ξ* are positive slack variables for the data points.…”

Section: Methodsmentioning

confidence: 99%

Prediction of theQ-eparameters from transition state structures

2013

Polym Eng Sci

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The Q–e scheme is remarkably useful in interpreting and predicting the reactivity of a monomer in free radical copolymerizations. In the present work, two support vector regression (SVR) models were developed to predict parameters Q and e in the Q–e scheme. Quantum chemical descriptors used to build SVR models were calculated, for the first time, from transition state species with structures C1H3—C2HR3• or •C1H2—C2H2R3, formed from vinyl monomer C1H2 C2HR3 + H•. The optimal ν‐SVR model of lnQ (C = 130, ν = 0.2, and γ = 1.0) based on 70 monomers has the root mean square (rms) error of 0.336 and correlation coefficient (R) of 0.982. The optimal ε‐SVR model of e (C = 1.2, γ = 3, and ε = 10−2) produces rms = 0.259 and R = 0.963. Compared with previous models, the SVM models in this article have better predictive performance. Results of the study suggest that calculating quantum chemical descriptors from the transition state structures to predict parameters Q and e in the Q–e scheme is feasible. This investigation encourages the further application of transition state descriptors to other quantitative structure–activity relationships (QSARs). POLYM. ENG. SCI., 53:2151–2158, 2013. © 2013 Society of Plastics Engineers

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“…Support vector machines (SVM) [14][15][16][17][18][19][20][21][22][23][24][25] are a powerful state-of-the-art data mining algorithm for nonlinear input-output knowledge discovery. In SVM, the idea is to map the input data into a high dimensional feature space and subsequently carry out the linear regression in the feature space.…”

Section: Support Vector Machinementioning

confidence: 99%

Support Vector Machine Regression for Reactivity Parameters of Vinyl Monomers

Yu¹,

Wang

Chen³

2011

J. Chil. Chem. Soc.

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Recently, the support vector machine (SVM), as a novel type of learning machine, has been introduced to solve chemical problems. In this study, ε-support vector regression (ε-SVR) and ν-support vector regression (ν-SVR) were, respectively, used to construct quantitative structure-property relationship (QSPR) models of Q and e parameters in the Q-e scheme, which is remarkably useful in the interpretation of the reactivity of a monomer in free-radical copolymerizations. The quantum chemical descriptors used to developed the SVR models were calculated from styrene and radicals with structures CH 3 CH 2 C 1The optimum ε-SVR model of lnQ (C= 9, ε =0.05 and γ =0.2) and the optimum ν-SVR model of e (C=100, ν = 0.5 and γ =0.4) produced low root mean square (rms) errors for prediction sets: 0.318 and 0.266, respectively. Thus, applying SVR to predict parameters Q and e is successful.

show abstract

Symbolization assisted SVM classifier for noisy data

Cited by 13 publications

References 17 publications

The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing

The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing

Prediction of theQ-eparameters from transition state structures

Support Vector Machine Regression for Reactivity Parameters of Vinyl Monomers

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