{"paper":{"title":"A Distance-Based Branch and Bound Feature Selection Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ari Frank, Dan Geiger, Zohar Yakhini","submitted_at":"2012-10-19T15:05:25Z","abstract_excerpt":"There is no known efficient method for selecting k Gaussian features from     n which achieve the lowest Bayesian classification error. We show an example     of how greedy algorithms faced with this task are led to give results that are     not optimal. This motivates us to propose a more robust approach. We present a     Branch and Bound algorithm for finding a subset of k independent Gaussian     features which minimizes the naive Bayesian classification error. Our algorithm     uses additive monotonic distance measures to produce bounds for the Bayesian     classification error in order to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.2488","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}