{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ahsen ME"],"funding":["IBM and National Institute for General Medical Sciences"],"pagination":["vbae093"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11249386"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["4(1)"],"pubmed_abstract":["Motivation
The integration of vast, complex biological data with computational models offers profound insights and predictive accuracy. Yet, such models face challenges: poor generalization and limited labeled data.Results
To overcome these difficulties in binary classification tasks, we developed the Method for Optimal Classification by Aggregation (MOCA) algorithm, which addresses the problem of generalization by virtue of being an ensemble learning method and can be used in problems with limited or no labeled data. We developed both an unsupervised (uMOCA) and a supervised (sMOCA) variant of MOCA. For uMOCA, we show how to infer the MOCA weights in an unsupervised way, which are optimal under the assumption of class-conditioned independent classifier predictions. When it"],"journal":["Bioinformatics advances"],"pubmed_title":["Optimal linear ensemble of binary classifiers."],"pmcid":["PMC11249386"],"funding_grant_id":["R01GM140287"],"pubmed_authors":["Ahsen ME","Vogel R","Stolovitzky G"],"additional_accession":[]},"is_claimable":false,"name":"Optimal linear ensemble of binary classifiers.","description":"Motivation
The integration of vast, complex biological data with computational models offers profound insights and predictive accuracy. Yet, such models face challenges: poor generalization and limited labeled data.Results
To overcome these difficulties in binary classification tasks, we developed the Method for Optimal Classification by Aggregation (MOCA) algorithm, which addresses the problem of generalization by virtue of being an ensemble learning method and can be used in problems with limited or no labeled data. We developed both an unsupervised (uMOCA) and a supervised (sMOCA) variant of MOCA. For uMOCA, we show how to infer the MOCA weights in an unsupervised way, which are optimal under the assumption of class-conditioned independent classifier predictions. When it","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024","modification":"2025-04-25T19:14:48.706Z","creation":"2025-04-06T07:54:12.573Z"},"accession":"S-EPMC11249386","cross_references":{"pubmed":["39011276"],"doi":["10.1093/bioadv/vbae093"]}}