The sparse model character of 1-norm penalty term of Least Absolute Shrinkage and Selection Operator (LASSO) can be applied to automatic feature selection. Since 1-norm SVM is also designed with 1-norm (LASSO) penalty term, this study labels it as LASSO for classification. This paper introduces the smooth technique into 1-norm SVM and calls it smooth LASSO for classification (SLASSO) to provide simultaneous classification and feature selection. In the experiments, we compare SLASSO with other

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... proaches of "wrapper" and "filter" models for feature selection. Results showed that SLASSO has slightly better accuracy than other approaches with the desirable ability of feature suppression.