{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/decision-stream-cultivating-deep-decision","title":"Decision Stream: Cultivating Deep Decision Trees","arxiv_id":"1704.07657","date":"2017-04-25","proceeding":null,"authors":["Dmitry Ignatov","Andrey Ignatov"],"abstract":"Various modifications of decision trees have been extensively used during the\npast years due to their high efficiency and interpretability. Tree node\nsplitting based on relevant feature selection is a key step of decision tree\nlearning, at the same time being their major shortcoming: the recursive nodes\npartitioning leads to geometric reduction of data quantity in the leaf nodes,\nwhich causes an excessive model complexity and data overfitting. In this paper,\nwe present a novel architecture - a Decision Stream, - aimed to overcome this\nproblem. Instead of building a tree structure during the learning process, we\npropose merging nodes from different branches based on their similarity that is\nestimated with two-sample test statistics, which leads to generation of a deep\ndirected acyclic graph of decision rules that can consist of hundreds of\nlevels. To evaluate the proposed solution, we test it on several common machine\nlearning problems - credit scoring, twitter sentiment analysis, aircraft flight\ncontrol, MNIST and CIFAR image classification, synthetic data classification\nand regression. Our experimental results reveal that the proposed approach\nsignificantly outperforms the standard decision tree learning methods on both\nregression and classification tasks, yielding a prediction error decrease up to\n35%.","url_abs":"http://arxiv.org/abs/1704.07657v3","url_pdf":"http://arxiv.org/pdf/1704.07657v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"decision-stream-cultivating-deep-decision","repo_url":"https://github.com/aiff22/Decision-Stream","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"decision-stream-cultivating-deep-decision","repo_url":"https://github.com/osman-haider/Decision-Tree-Stream","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"twitter-sentiment-analysis","task_name":"Twitter Sentiment Analysis"},{"task_slug":"feature-selection","task_name":"feature selection"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1704.07657","atlas_url":"https://app.syntology.ai/?focus=1704.07657","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}