Best Paper Presentation on Clusterwise Linear Regression (ML Spring 2021) скачать в хорошем качестве

Best Paper Presentation on Clusterwise Linear Regression (ML Spring 2021)

#BestPaperPresentation #MachineLearning #Regression 🏆 Presenters 🏆 Tolgahan Türker, Aslı Sarı, Saleh Msaddi, Yasin Burak Bilim and Abdulsalam Yazid Bande; Istanbul Technical University (Machine Learning course project, Best Project Presentation). Course lecture playlist:    • Machine Learning Blink 1.1 (types of learn...   In this video, we are presenting the following research paper: Gitman, I., Chen, J., Lei, E. and Dubrawski, A., 2018. Novel prediction techniques based on clusterwise linear regression. arXiv preprint arXiv:1804.10742. ** Link to the research paper: https://arxiv.org/pdf/1804.10742v1.pdf ** Link to Github code of the paper: https://github.com/Kipok/clr_prediction ** Link to the modified code of the paper: https://drive.google.com/drive/folder... ************************ Paper abstract: In this paper we explore different regression models based on Clusterwise Linear Regression (CLR). CLR aims to find the partition of the data into k clusters, such that linear regressions fitted to each of the clusters minimize overall mean squared error on the whole data. The main obstacle preventing to use found regression models for prediction on the unseen test points is the absence of a reasonable way to obtain CLR cluster labels when the values of target variable are unknown. In this paper we propose two novel approaches on how to solve this problem. The first approach, predictive CLR builds a separate classification model to predict test CLR labels. The second approach, constrained CLR utilizes a set of user-specified constraints that enforce certain points to go to the same clusters. Assuming the constraint values are known for the test points, they can be directly used to assign CLR labels. We evaluate these two approaches on three UCI ML datasets as well as on a large corpus of health insurance claims. We show that both of the proposed algorithms significantly improve over the known CLR-based regression methods. Moreover, predictive CLR consistently outperforms linear regression and random forest, and shows comparable performance to support vector regression on UCI ML datasets. The constrained CLR approach achieves the best performance on the health insurance dataset, while enjoying only ≈ 20 times increased computational time over linear regression. ************************