Probabilistic Classification Vector Machine for Multi-Class Classification

• URL: http://arxiv.org/abs/2006.15791v1
• Date: Mon, 29 Jun 2020 03:21:38 GMT
• Title: Probabilistic Classification Vector Machine for Multi-Class Classification
• Authors: Shengfei Lyu, Xing Tian, Yang Li, Bingbing Jiang, Huanhuan Chen
• Abstract summary: The probabilistic classification vector machine (PCVM) synthesizes the advantages of both the support vector machine and the relevant vector machine. We extend the PCVM to multi-class cases via voting strategies such as one-vs-rest or one-vs-one. Two learning algorithms, i.e., one top-down algorithm and one bottom-up algorithm, have been implemented in the mPCVM. The superior performance of the mPCVMs is extensively evaluated on synthetic and benchmark data sets.
• Score: 29.411892651468797
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The probabilistic classification vector machine (PCVM) synthesizes the advantages of both the support vector machine and the relevant vector machine, delivering a sparse Bayesian solution to classification problems. However, the PCVM is currently only applicable to binary cases. Extending the PCVM to multi-class cases via heuristic voting strategies such as one-vs-rest or one-vs-one often results in a dilemma where classifiers make contradictory predictions, and those strategies might lose the benefits of probabilistic outputs. To overcome this problem, we extend the PCVM and propose a multi-class probabilistic classification vector machine (mPCVM). Two learning algorithms, i.e., one top-down algorithm and one bottom-up algorithm, have been implemented in the mPCVM. The top-down algorithm obtains the maximum a posteriori (MAP) point estimates of the parameters based on an expectation-maximization algorithm, and the bottom-up algorithm is an incremental paradigm by maximizing the marginal likelihood. The superior performance of the mPCVMs, especially when the investigated problem has a large number of classes, is extensively evaluated on synthetic and benchmark data sets.

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