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Multi-dimensional classification via stacked dependency exploitation
Science China Information Sciences ( IF 8.8 ) Pub Date : 2020-11-09 , DOI: 10.1007/s11432-019-2905-3
Bin-Bin Jia , Min-Ling Zhang

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.



中文翻译:

通过堆栈依赖开发进行多维分类

多维分类(MDC)旨在同时为多个异构类空间建立分类模型,其中每个类空间通过一个特定维度来表征对象的语义。对类空间之间的依赖关系进行建模在解决MDC任务中起着关键作用,其中大多数方法通过在类空间上假设有向无环图(DAG)结构或随机链结构来起作用。与现有的概率策略不同,本文通过堆叠依赖开发提出了一种用于依赖模型的确定性策略SEEM。在第一级中,考虑了成对依赖关系,与通过DAG或链接结构在所有类空间中对完全依赖关系建模相比,可以更可靠地对其进行建模。在第二层中,看不见的实例wrt的类标签 每个类别空间是通过自适应堆叠第一级成对分类器的预测输出来确定的。实验结果表明,与最新的MDC方法相比,堆叠式依赖开发具有更高的性能。

更新日期:2020-11-16
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