Comput Math Methods Med - Iterative reweighted noninteger norm regularizing SVM for gene expression data classification.

Tópicos

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Resumo

Support vector machine is an effective classification and regression method that uses machine learning theory to maximize the predictive accuracy while avoiding overfitting of data. L2 regularization has been commonly used. If the training dataset contains many noise variables, L1 regularization SVM will provide a better performance. However, both L1 and L2 are not the optimal regularization method when handing a large number of redundant values and only a small amount of data points is useful for machine learning. We have therefore proposed an adaptive learning algorithm using the iterative reweighted p-norm regularization support vector machine for 0 < p = 2. A simulated data set was created to evaluate the algorithm. It was shown that a p value of 0.8 was able to produce better feature selection rate with high accuracy. Four cancer data sets from public data banks were used also for the evaluation. All four evaluations show that the new adaptive algorithm was able to achieve the optimal prediction error using a p value less than L1 norm. Moreover, we observe that the proposed Lp penalty is more robust to noise variables than the L1 and L2 penalties.

Resumo Limpo

support vector machin effect classif regress method use machin learn theori maxim predict accuraci avoid overfit data l regular common use train dataset contain mani nois variabl l regular svm will provid better perform howev l l optim regular method hand larg number redund valu small amount data point use machin learn therefor propos adapt learn algorithm use iter reweight pnorm regular support vector machin p simul data set creat evalu algorithm shown p valu abl produc better featur select rate high accuraci four cancer data set public data bank use also evalu four evalu show new adapt algorithm abl achiev optim predict error use p valu less l norm moreov observ propos lp penalti robust nois variabl l l penalti

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