{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T04:28:50Z","timestamp":1729225730055,"version":"3.27.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"Ordinal tabular data, with advantages of structured knowledge representation in tabular data and the characteristic of inter-class ranks, has drawn increasing attention. However, existing retrieval-based tabular deep learning methods designed primarily for classical tabular data pay less attention to ordinal tabular data. Ordinal knowledge of ordinal tabular data provides a more explicit objective for tabular ordinal classification by considering both classification and regression properties. Furthermore, these approaches overlook the significance of intra-class group features which can balance the retrieved probability of various sample size groups and capture shared knowledge among multiple samples within same group. In this work, we propose the Intra-Class Groups Retrieval and Inter-Class Ordinal Knowledge Augmented Network (TabCGOK) model for ordinal tabular data prediction, equipped with Intra-Class Groups Retrieval (CG) module and Inter-Class Ordinal Knowledge Augmented (OK) module. The CG module provides intra-class group features candidate set for subsequent retrieval operation. It divides each class into several groups, then extracts the representation of each group as intra-class group features. And the intra-class group features candidate set consists of all intra-class group features from each class. The OK module is designed to capture inter-class ordinal knowledge. It estimates the ordinal distances by calculating inter-class feature distances, which could correspond to the inter-class non-isometric nature of ordinal knowledge, and then aggregates the previous ordinal distances to clarify the containment relationship of ordinal knowledge. OK module utilizes the attention mechanism for fusing the captured ordinal knowledge to retrieved intra-class group features. Finally, TabCGOK integrates fused intra-class group features with sample level features for ordinal tabular data prediction. Extensive experiments on several ordinal tabular datasets demonstrate the effectiveness of our method. The source code is available at https:\/\/github.com\/luozhengdong\/TabCGOK.<\/jats:p>","DOI":"10.3233\/faia240746","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:17:08Z","timestamp":1729171028000},"source":"Crossref","is-referenced-by-count":0,"title":["TabCGOK: Intra-Class Groups Retrieval and Inter-Class Ordinal Knowledge Augmented Network for Ordinal Tabular Data Prediction"],"prefix":"10.3233","author":[{"given":"Zhengdong","family":"Luo","sequence":"first","affiliation":[{"name":"Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China"}]},{"given":"Abibulla","family":"Atawulla","sequence":"additional","affiliation":[{"name":"Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China"}]},{"given":"Fengyi","family":"Yang","sequence":"additional","affiliation":[{"name":"Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China"}]},{"given":"Yongqing","family":"Zhu","sequence":"additional","affiliation":[{"name":"Hithink RoyalFlush Information Network Co., Ltd. Hangzhou 310023, China"}]},{"given":"Yixiao","family":"Ren","sequence":"additional","affiliation":[{"name":"Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China"}]},{"given":"Yunfei","family":"Han","sequence":"additional","affiliation":[{"name":"Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China"}]},{"given":"Xi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240746","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:17:09Z","timestamp":1729171029000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240746"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240746","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}