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We consider classification tasks in the regime of scarce labeled trainingdata in high dimensional feature space, where specific expert knowledge is alsoavailable. We propose a new hybrid optimization algorithm that solves theelastic-net support vector machine (SVM) through an alternating directionmethod of multipliers in the first phase, followed by an interior-point methodfor the classical SVM in the second phase. Both SVM formulations are adapted toknowledge incorporation. Our proposed algorithm addresses the challenges ofautomatic feature selection, high optimization accuracy, and algorithmicflexibility for taking advantage of prior knowledge. We demonstrate theeffectiveness and efficiency of our algorithm and compare it with existingmethods on a collection of synthetic and real-world data.

HIPAD - A Hybrid Interior-Point Alternating Direction algorithm for knowledge-based SVM and feature selection