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Multi-dimensional classification via stacked dependency exploitation

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Abstract

Multi-dimensional classification (MDC) aims to build classification models for multiple heterogenous class spaces simultaneously, where each class space characterizes the semantics of an object w.r.t. one specific dimension. Modeling dependencies among class spaces plays a key role in solving MDC tasks, where most approaches work by assuming directed acyclic graph (DAG) structure or random chaining structure over class spaces. Different from existing probabilistic strategies, a deterministic strategy named SEEM for dependency modeling is proposed in this paper via stacked dependency exploitation. In the first-level, pairwise dependencies are considered which can be modeled more reliably than modeling full dependencies among all class spaces by DAG or chaining structure. In the second-level, the class label of unseen instance w.r.t. each class space is determined by adaptively stacking predictive outputs from first-level pairwise classifiers. Experimental results show that stacked dependency exploitation leads to superior performance against state-of-the-art MDC approaches.

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Acknowledgements

This work was supported by National Key R&D Program of China (Grant No. 2018YFB1004300), China University S&T Innovation Plan Guided by the Ministry of Education, and partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization. The authors wish to thank the associate editor and anonymous reviewers for their helpful comments and suggestions.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China

    Bin-Bin Jia & Min-Ling Zhang

  2. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

    Bin-Bin Jia

  3. Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, China

    Bin-Bin Jia & Min-Ling Zhang

  4. Collaborative Innovation Center of Wireless Communications Technology, Nanjing, 210096, China

    Min-Ling Zhang

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  1. Bin-Bin Jia
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  2. Min-Ling Zhang
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Correspondence to Min-Ling Zhang.

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Jia, BB., Zhang, ML. Multi-dimensional classification via stacked dependency exploitation. Sci. China Inf. Sci. 63, 222102 (2020). https://doi.org/10.1007/s11432-019-2905-3

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  • DOI: https://doi.org/10.1007/s11432-019-2905-3

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