Multiclass Probabilistic Classification Vector Machine.

Clicks: 348
ID: 66011
2019
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Ranked #19 of 56 articles by views in IEEE Transactions on Neural Networks and Learning Systems

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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 multiclass 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 multiclass PCVM (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 ita 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 the synthetic and benchmark data sets.
Reference Key
lyu2019multiclassieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lyu, Shengfei;Tian, Xing;Li, Yang;Jiang, Bingbing;Chen, Huanhuan;
Journal IEEE Transactions on Neural Networks and Learning Systems
Year 2019
DOI
10.1109/TNNLS.2019.2947309
URL
Keywords
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