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Description
| - 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’s 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.
- 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’s 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)
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Title
| - Explaining Anomalies with Sapling Random Forests
- Explaining Anomalies with Sapling Random Forests (en)
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skos:prefLabel
| - Explaining Anomalies with Sapling Random Forests
- Explaining Anomalies with Sapling Random Forests (en)
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skos:notation
| - RIV/68407700:21230/14:00219644!RIV15-MSM-21230___
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
| - I, P(GA13-17187S), P(GPP103/12/P514), S
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http://linked.open...vai/riv/dodaniDat
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http://linked.open...aciTvurceVysledku
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http://linked.open.../riv/druhVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...titaPredkladatele
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http://linked.open...dnocenehoVysledku
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http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/14:00219644
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - Anomaly explanation; decision trees; feature selection; random forest (en)
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http://linked.open.../riv/klicoveSlovo
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http://linked.open...ontrolniKodProRIV
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http://linked.open...v/mistoKonaniAkce
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http://linked.open...i/riv/mistoVydani
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http://linked.open...i/riv/nazevZdroje
| - Proceedings of the 14th conference ITAT 2014 – Workshops and Posters
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http://linked.open...in/vavai/riv/obor
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http://linked.open...ichTvurcuVysledku
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http://linked.open...cetTvurcuVysledku
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http://linked.open...vavai/riv/projekt
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http://linked.open...UplatneniVysledku
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http://linked.open...iv/tvurceVysledku
| - Pevný, Tomáš
- Kopp, Martin
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http://linked.open...vavai/riv/typAkce
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http://linked.open.../riv/zahajeniAkce
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number of pages
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http://purl.org/ne...btex#hasPublisher
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https://schema.org/isbn
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http://localhost/t...ganizacniJednotka
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