Categorical variables often appear in datasets for classification and regression tasks, and they need to be encoded into numerical values before training. Since many encoders have been developed and can significantly impact performance, choosing the appropriate encoder for a task becomes a time-consuming yet important practical issue. This study broadly classifies machine learning models into three categories: 1) ATI models that implicitly perform affine transformations on inputs, such as multi-layer perceptron neural network; 2) Tree-based models that are based on decision trees, such as random forest; and 3) the rest, such as kNN. Theoretically, we prove that the one-hot encoder is the best choice for ATI models in the sense that it can mimic any other encoders by learning suitable weights from the data. We also explain why the target encoder and its variants are the most suitable encoders for tree-based models. This study conducted comprehensive computational experiments to evaluate 14 encoders, including one-hot and target encoders, along with eight common machine-learning models on 28 datasets. The computational results agree with our theoretical analysis. The findings in this study shed light on how to select the suitable encoder for data scientists in fields such as fraud detection, disease diagnosis, etc.
Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems
University of Southampton（南安普顿大学）；Centre Recerca Matematica（数学研究中心）；Universitat Autonoma de Barcelona（巴塞罗那自治大学）；Universitat Ramon Llull（拉蒙·柳利大学）；ESADE（ESADE商学院）；Universitat de Barcelona（巴塞罗那大学）
Interpretable vs Learned Encoders for High-Cardinality Fraud Detection
Goizueta Business School Emory University Atlanta, GA, USA（埃默里大学戈伊苏埃塔商学院）；Computer Sciences University of California, Berkeley Kirkland, WA（加州大学伯克利分校计算机科学系）；Stern School of Business New York University New York, NY, USA（纽约大学斯特恩商学院）；School of Data Science University of Pennsylvania Philadelphia, PA, USA（宾夕法尼亚大学数据科学学院）；Pratt School of Engineering Duke University Durham, NC, USA（达特茅斯大学普拉特工程学院）
在IEEE-CIS欺诈数据集上比较7种分类编码方法，实体嵌入在AUC-ROC上最优（0.9612），与CatBoost（0.9602）无显著差异，优于其他编码器；CatBoost在AUC-PR上领先（0.822 vs 0.793），无编码器同时主导两个指标。
A Comparative Study on Affective Cues in Text Embeddings Across Psychological Emotion Theories
Department of Electronics, Information, and Bioengineering, Polytechnic University of Milan（米兰理工学院电子、信息与生物工程系）；Multimedia Mining and Search Group, Institute of Computational Perception, Johannes Kepler University Linz（莱茵河畔林茨约翰·凯撒大学计算感知研究所多媒体挖掘与检索小组）；Department of Music Pedagogy, Nuremberg University of Music（纽伦堡音乐学院音乐教育系）；Human-centered AI Group, AI Lab, Linz Institute of Technology（林茨技术学院以人为本的人工智能小组）
Evaluating Named Entity Recognition: A comparative analysis of mono- and multilingual transformer models on a novel Brazilian corporate earnings call transcripts dataset
Instituto Federal de São Paulo - IFSP（圣保罗联邦教育、科学和技术研究所（IFSP））；Universidade Estadual de Campinas - Unicamp（坎皮纳斯州立大学（Unicamp））
