Although Regularized Multiple Criteria Linear Programming (RMCLP) model has shown its effectiveness in classification problems, its inherent drawback of linear formulation limits itself into only solving linear classification problems. To extend RMCLP into solving non-linear problems, in this paper, we propose a kernel based RMCLP model by using a form w = N/∑(i=2)β_iΦ(x_i) to replace the original weight w in RMCLP model. Empirical studies on synthetic and real-life datasets demonstrate that our new model is capable to classify non-linear datasets. Moreover, comparisons to SVM and MCQP also exhibit the fact that our new model is superior to other non-linear models in classification problems.
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机译：虽然正规化的多标准线性编程（RMCLP）模型在分类问题中显示了其有效性，但线性制定的固有缺点仅限于求解线性分类问题。为了将RMCLP扩展到求解非线性问题，在本文中，我们通过使用形式W = n /Σ（i = 2）β_iφ（x_i）来提出基于内核的RMCLP模型来替换RMCLP模型中的原始权重W.合成和现实生活数据集的实证研究表明，我们的新模型能够对非线性数据集进行分类。此外，对SVM和MCQP的比较也表现出我们的新模型优于分类问题的其他非线性模型。
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