{"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/towards-wide-learning-experiments-in","title":"Towards Wide Learning: Experiments in Healthcare","arxiv_id":"1612.05730","date":"2016-12-17","proceeding":null,"authors":["Snehasis Banerjee","Tanushyam Chattopadhyay","Swagata Biswas","Rohan Banerjee","Anirban Dutta Choudhury","Arpan Pal","Utpal Garain"],"abstract":"In this paper, a Wide Learning architecture is proposed that attempts to\nautomate the feature engineering portion of the machine learning (ML) pipeline.\nFeature engineering is widely considered as the most time consuming and expert\nknowledge demanding portion of any ML task. The proposed feature recommendation\napproach is tested on 3 healthcare datasets: a) PhysioNet Challenge 2016\ndataset of phonocardiogram (PCG) signals, b) MIMIC II blood pressure\nclassification dataset of photoplethysmogram (PPG) signals and c) an emotion\nclassification dataset of PPG signals. While the proposed method beats the\nstate of the art techniques for 2nd and 3rd dataset, it reaches 94.38% of the\naccuracy level of the winner of PhysioNet Challenge 2016. In all cases, the\neffort to reach a satisfactory performance was drastically less (a few days)\nthan manual feature engineering.","url_abs":"http://arxiv.org/abs/1612.05730v2","url_pdf":"http://arxiv.org/pdf/1612.05730v2.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":"towards-wide-learning-experiments-in","repo_url":"https://github.com/sayakpaul/Generating-categories-from-arXiv-paper-titles","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"emotion-classification","task_name":"Emotion Classification"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}