@inproceedings{77905e224bc745d3817ced43c85065a1,
title = "ANNC: AUC-based feature selection by maximizing nearest neighbor complementarity",
abstract = "Feature selection is crucial for dimension reduction. Dozens of approaches employ the area under ROC curve, i.e., AUC, to evaluate features, and have shown their attractiveness in finding discriminative targets. However, feature complementarity for jointly discriminating classes is generally improperly handled by these approaches. In a recent approach to deal with such issues, feature complementarity was evaluated by computing the difference between the neighbors of each instance in different feature dimensions. This local-learning based approach introduces a distinctive way to determine how a feature is complementarily discriminative given another. Nevertheless, neighbor information is usually sensitive to noises. Furthermore, evaluating merely one-side information of nearest misses will definitely neglect the impacts of nearest hits on feature complementarity. In this paper, we propose to integrate all-side local-learning based complementarity into an AUC-based approach, dubbed ANNC, to evaluate pairwise features by scrutinizing their comprehensive misclassification information in terms of both k-nearest misses and k-nearest hits. This strategy contributes to capture complementary features that collaborate with each other to achieve remarkable recognition performance. Extensive experiments on openly available benchmarks demonstrate the effectiveness of the new approach under various metrics.",
keywords = "AUC, All-side recognition, Feature complementarity, Feature selection, Nearest neighbors",
author = "Xuemeng Jiang and Jun Wang and Jinmao Wei and Jianhua Ruan and Gang Yu",
note = "Funding Information: Acknowledgments. This work was supported in part by the National Natural Science Foundation of China under Grant No. 61772288, the Science Foundation of Tianjin China under Grant No. 18JCZDJC30900. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 15th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018 ; Conference date: 28-08-2018 Through 31-08-2018",
year = "2018",
doi = "10.1007/978-3-319-97304-3_59",
language = "English (US)",
isbn = "9783319973036",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "772--785",
editor = "Byeong-Ho Kang and Xin Geng",
booktitle = "PRICAI 2018",
}