{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T10:20:38Z","timestamp":1760523638378},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>Attaining the proper balance between underfitting and overfitting is one of the central challenges in machine learning. It has been approached mostly by deriving bounds on generalization risks of learning algorithms. Such bounds are, however, rarely controllable. In this study, a novel bias-variance balancing objective function is introduced in order to improve generalization performance. By utilizing distance correlation, this objective function is able to indirectly control a stability-based upper bound on a model's expected true risk. In addition, the Generalization-Aware Collaborative Ensemble Regressor (GLACER) is developed, a model that bags a crowd of structured regression models, while allowing them to collaborate in a fashion that minimizes the proposed objective function. The experimental results on both synthetic and real-world data indicate that such an objective enhances the overall model's predictive performance. When compared against a broad range of both traditional and structured regression models GLACER was ~10-56% and ~49-99% more accurate for the task of predicting housing prices and hospital readmissions, respectively.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/363","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:49:10Z","timestamp":1530755350000},"page":"2616-2622","source":"Crossref","is-referenced-by-count":4,"title":["Generalization-Aware Structured Regression towards Balancing Bias and Variance"],"prefix":"10.24963","author":[{"given":"Martin","family":"Pavlovski","sequence":"first","affiliation":[{"name":"Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, PA, USA"},{"name":"Macedonian Academy of Sciences and Arts, Skopje, Republic of Macedonia"}]},{"given":"Fang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, PA, USA"}]},{"given":"Nino","family":"Arsov","sequence":"additional","affiliation":[{"name":"Macedonian Academy of Sciences and Arts, Skopje, Republic of Macedonia"}]},{"given":"Ljupco","family":"Kocarev","sequence":"additional","affiliation":[{"name":"Macedonian Academy of Sciences and Arts, Skopje, Republic of Macedonia"}]},{"given":"Zoran","family":"Obradovic","sequence":"additional","affiliation":[{"name":"Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, PA, USA"}]}],"member":"10584","event":{"number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2018","name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","start":{"date-parts":[[2018,7,13]]},"theme":"Artificial Intelligence","location":"Stockholm, Sweden","end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:52:00Z","timestamp":1530755520000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/363"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/363","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}