Multi-label learning has emerged as a crucial paradigm in data analysis,
addressing scenarios where instances are associated with multiple class labels
simultaneously. With the growing prevalence of multi-label data across diverse
applications, such as text and image classification, the significance of
multi-label feature selection has become increasingly evident. This paper
presents a novel information-theoretical filter-based multi-label feature
selection, called ATR, with a new heuristic function. Incorporating a
combinations of algorithm adaptation and problem transformation approaches, ATR
ranks features considering individual labels as well as abstract label space
discriminative powers. Our experimental studies encompass twelve benchmarks
spanning various domains, demonstrating the superiority of our approach over
ten state-of-the-art information-theoretical filter-based multi-label feature
selection methods across six evaluation metrics. Furthermore, our experiments
affirm the scalability of ATR for benchmarks characterized by extensive feature
and label spaces. The codes are available at this https URL

该论文介绍了一种基于信息论的多标签特征选择方法 ATR，通过算法适应性和问题转换方法的组合，能够根据个别标签和抽象标签空间的区分能力对特征进行排名，并且在六个评估指标上优于其他十种信息论滤波器的多标签特征选择方法。

自适应和转换相关性的多标签特征选择

Multi-Label Feature Selection Using Adaptive and Transformed Relevance

In this paper, we propose an additionsubtraction twin-gated recurrent network
(ATR) to simplify neural machine translation. The recurrent units of ATR are
heavily simplified to have the smallest number of weight matrices among units
of all existing gated RNNs. With the simple addition and subtraction operation,
we introduce a twin-gated mechanism to build input and forget gates which are
highly correlated. Despite this simplification, the essential non-linearities
and capability of modeling long-distance dependencies are preserved.
Additionally, the proposed ATR is more transparent than LSTM/GRU due to the
simplification. Forward self-attention can be easily established in ATR, which
makes the proposed network interpretable. Experiments on WMT14 translation
tasks demonstrate that ATR-based neural machine translation can yield
competitive performance on English- German and English-French language pairs in
terms of both translation quality and speed. Further experiments on NIST
Chinese-English translation, natural language inference and Chinese word
segmentation verify the generality and applicability of ATR on different
natural language processing tasks.

本文提出了一种称为 ATR 的附加减法双门控循环网络，其采用了双门机制来构建高度相关的输入门和遗忘门。通过使用简单的加法和减法操作对循环单元进行了大幅简化，但保留了关键的非线性和长距离依赖建模能力。实验结果表明，ATR 在 WMT14 翻译任务中表现出竞争力，在英德和英法语种对的翻译质量和速度上均取得了良好的表现，并且在不同的自然语言处理任务中都表现出了通用性和适用性。