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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Multilabel Classification Using Error-Correcting Codes of Hard or Soft Bits
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Multilabel Classification Using Error-Correcting Codes of Hard or Soft Bits

机译：使用硬位或软位的纠错码进行多标签分类

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摘要

We formulate a framework for applying errorcorrecting codes (ECCs) on multilabel classification problems. The framework treats some base learners as noisy channels and uses ECC to correct the prediction errors made by the learners. The framework immediately leads to a novel ECC-based explanation of the popular random k-label sets (RAKEL) algorithm using a simple repetition ECC. With the framework, we empirically compare a broad spectrum of off-the-shelf ECC designs for multilabel classification. The results not only demonstrate that RAKEL can be improved by applying some stronger ECC, but also show that the traditional binary relevance approach can be enhanced by learning more parity-checking labels. Our research on different ECCs also helps to understand the tradeoff between the strength of ECC and the hardness of the base learning tasks. Furthermore, we extend our research to ECC with either hard (binary) or soft (real-valued) bits by designing a novel decoder. We demonstrate that the decoder improves the performance of our framework.
机译：我们制定了一个框架，用于在多标签分类问题上应用纠错码（ECC）。该框架将一些基础学习者视为嘈杂的渠道，并使用ECC来纠正学习者所犯的预测错误。该框架立即导致使用简单的重复ECC对流行的随机k标签集（RAKEL）算法进行基于ECC的新颖解释。通过该框架，我们从经验上比较了用于多标签分类的各种现成的ECC设计。结果不仅表明可以通过应用一些更强大的ECC来改善RAKEL，而且还表明可以通过学习更多的奇偶校验标签来增强传统的二进制相关性方法。我们对不同ECC的研究也有助于理解ECC的强度与基础学习任务的难度之间的折衷。此外，通过设计新颖的解码器，我们将研究范围扩展到了具有硬（二进制）位或软（实值）位的ECC。我们证明了解码器改善了我们框架的性能。

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