. "Dem\u00E4novsk\u00E1 Dolina" . "RIV/68407700:21230/14:00219644" . . "http://www.library.sk/i2/content.csg.cls?ictx=cav&repo=crepo1&key=70202320092" . "Explaining Anomalies with Sapling Random Forests"@en . . . . . "8"^^ . "21230" . "Pevn\u00FD, Tom\u00E1\u0161" . "[D6E3C2D9B1C5]" . . "Anomaly explanation; decision trees; feature selection; random forest"@en . . "16067" . "Kopp, Martin" . "2"^^ . . . "RIV/68407700:21230/14:00219644!RIV15-MSM-21230___" . . "The main objective of anomaly detection algo- rithms is finding samples deviating from the majority. Al- though a vast number of algorithms designed for this al- ready exist, almost none of them explain, why a particular sample was labelled as an anomaly. To address this is- sue, we propose an algorithm called Explainer, which re- turns the explanation of sample\u2019s differentness in disjunc- tive normal form (DNF), which is easy to understand by humans. Since Explainer treats anomaly detection algo- rithms as black-boxes, it can be applied in many domains to simplify investigation of anomalies. The core of Explainer is a set of specifically trained trees, which we call sapling random forests. Since their training is fast and memory efficient, the whole algorithm is lightweight and applicable to large databases, data- streams, and real-time problems. The correctness of Ex- plainer is demonstrated on a wide range of synthetic and real world datasets." . "2"^^ . . "Explaining Anomalies with Sapling Random Forests" . "Praha" . . "2014-09-25+02:00"^^ . "\u00DAstav informatiky AV \u010CR" . . "Explaining Anomalies with Sapling Random Forests" . "The main objective of anomaly detection algo- rithms is finding samples deviating from the majority. Al- though a vast number of algorithms designed for this al- ready exist, almost none of them explain, why a particular sample was labelled as an anomaly. To address this is- sue, we propose an algorithm called Explainer, which re- turns the explanation of sample\u2019s differentness in disjunc- tive normal form (DNF), which is easy to understand by humans. Since Explainer treats anomaly detection algo- rithms as black-boxes, it can be applied in many domains to simplify investigation of anomalies. The core of Explainer is a set of specifically trained trees, which we call sapling random forests. Since their training is fast and memory efficient, the whole algorithm is lightweight and applicable to large databases, data- streams, and real-time problems. The correctness of Ex- plainer is demonstrated on a wide range of synthetic and real world datasets."@en . . . "I, P(GA13-17187S), P(GPP103/12/P514), S" . . "978-80-87136-19-5" . . "Proceedings of the 14th conference ITAT 2014 \u2013 Workshops and Posters" . . . . "Explaining Anomalies with Sapling Random Forests"@en .