剛性與柔性解碼之錯誤更正碼於多標籤分類學習之應用

Abstract

我們提出一個將錯誤更正碼 (error-correcting codes, ECC) 應用於多標籤分類問題 (multi-label classification) 的架構。 在這個架構中，我們以一些基礎學習器 (base learner) 當作有干擾的傳輸頻道， 並用錯誤更正碼來更正這些基礎學習器的預測錯誤。 透過這個架構，我們可以用簡單的重複碼 (repetition code) 來解釋現有的隨機 k 標籤組演算法 (random k-label-sets, RAKEL) 。 我們也實驗了各種錯誤更正碼應用在多標籤分類問題的效果， 實驗結果顯示，利用較強的錯誤更正碼可以改善隨機 k 標籤組演算法的表現； 此外，讓傳統的二元關聯演算法 (binary relevance) 學習一些校驗標籤 (parity-checking labels) 也會讓它有更好的表現。 而且，由不同的錯誤更正碼的實驗結果可以看出，錯誤更正碼的強度會影響基礎學習器的難度，妥善平衡兩者可以讓結果變得更好。 最後，我們也設計了一個新的解碼器來處理剛性（二元值）與柔性（實數值）的線性錯誤更正碼， 實驗結果也證實這個新的解碼器可以提昇這個架構的表現。

We formulate a framework for applying error-correcting codes (ECC) on multi-label classification problems. The framework treats some base learners as noisy channels and uses ECC to correct the prediction errors made by the learners. An immediate use of the framework is a novel ECC-based explanation of the popular random k-label-sets (RAKEL) algorithm using a simple repetition ECC. Using the framework, we empirically compare a broad spectrum of ECC designs for multi-label 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 study on different ECC also helps understand the trade-off between the strength of ECC and the hardness of the base learning tasks. Furthermore, we extend our study to linear ECC for either hard (binary) or soft (real-valued) bits, and design a novel decoder for the ECC. We demonstrate that the decoder improves the performance of our framework.