Abstract
We formulate a framework for applying error-correcting 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.
Highlights
M ULTI-LABEL classification is an extension of traditional multi-class classification
We presented a framework for applying the Error-correcting code (ECC) on multilabel classification
We studied the use of four classic ECC designs, namely the RAKEL repetition code (RREP), Hamming on Repetition (HAMR), BCH, and LDPC
Summary
M ULTI-LABEL classification is an extension of traditional multi-class classification. When the desired signal block is the single label (of some instances) and the noisy channel consists of some binary classifiers, it has been shown that a suitable use of the ECC could improve the association (prediction) accuracy of multi-class classification [4]. The four designs cover the simplest ECC idea to the state-of-the-art ECC in communication systems Such a framework allows us to give a novel ECC-based explanation to the random klabel sets (RAKEL) algorithm, which is popular for multilabel classification. The experimental results show that this decoder improves the performance of the ECC framework with soft inputs. The paper is the core of the first author’s M.S. thesis [10]
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