| Résumé | Many real-world classification problems (biomedical among them) are represented by very sparse and high dimensional datasets. Due to the sparsity of the data, the selection of classification models is strongly influenced by the characteristics of the particular dataset under study. If the class differences are not appreciable and are masked by spurious differences arising because of the peculiarities of the dataset, then the robustness/stability of the discovered feature subset is difficult to assess. The final classification rules learned on such subsets may generalize poorly. The difficulties may be partially alleviated by choosing an appropriate learning strategy. The recent success of the linear programming support vector machine (Liknon) for feature selection motivated us to analyze Liknon in more depth, particularly as it applies to multivariate sparse data. The efficiency of Liknon as a feature filter arises because of its ability to identify subspaces of the original feature space that increase class separation, controlled by a regularization parameter related to the margin between classes. We use an approach, inspired by the concept of transvariation intensity, for establishing a relation between the data, the regularization parameter and the margin. We discuss a computationally effective way of finding a classification model, coupled with feature selection. Throughout the paper we contrast Liknonbased classification model selection to the related Svmpath algorithm, which computes a full regularization path. |
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