FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285108895> ?p ?o ?g. }

• W4285108895 endingPage "613" @default.
• W4285108895 startingPage "597" @default.
• W4285108895 abstract "Nowadays, businesses in many industries face an increasing flow of data and information. Data are at the core of the decision-making process, hence it is vital to ensure that the data are of high quality and no noise is present. Outlier detection methods are aimed to find unusual patterns in data and find their applications in many practical domains. These methods employ different techniques, ranging from pure statistical tools to deep learning models that have gained popularity in recent years. Moreover, one of the most popular outlier detection techniques are machine learning models. They have several characteristics which affect the potential of their usefulness in real-life scenarios. The goal of this paper is to add to the existing body of research on outlier detection by comparing the isolation forest, DBSCAN and LOF techniques. Thus, we investigate the research question: which ones of these outlier detection models perform best in practical business applications. To this end, three models are built on 12 datasets and compared using 5 performance metrics. The final comparison of the models is based on the McNemar’s test, as well as on ranks per performance measure and on average. Three main conclusions can be made from the benchmarking study. First, the models considered in this research disagree differently, i.e. their type I and type II errors are not similar. Second, considering the time, AUPRC and sensitivity metrics, the iForest model is ranked the highest. Hence, the iForest model is the best in the cases when time performance is a key consideration as well as when the opportunity costs of not detecting an outlier are high. Third, the DBSCAN model obtains the highest ranking along the F1 score and precision dimensions. That allows us to conclude that if raising many false alarms is not an important concern, the DBSCAN model is the best to employ." @default.
• W4285108895 created "2022-07-14" @default.
• W4285108895 creator A5037079840 @default.
• W4285108895 creator A5049729997 @default.
• W4285108895 creator A5081225515 @default.
• W4285108895 creator A5086124843 @default.
• W4285108895 date "2022-01-01" @default.
• W4285108895 modified "2023-09-23" @default.
• W4285108895 title "Benchmarking Conventional Outlier Detection Methods" @default.
• W4285108895 cites W1991357106 @default.
• W4285108895 cites W1995443851 @default.
• W4285108895 cites W2000661457 @default.
• W4285108895 cites W2009481304 @default.
• W4285108895 cites W2097714558 @default.
• W4285108895 cites W2122646361 @default.
• W4285108895 cites W2144182447 @default.
• W4285108895 cites W2150847526 @default.
• W4285108895 cites W2165394871 @default.
• W4285108895 cites W2217007515 @default.
• W4285108895 cites W2296719434 @default.
• W4285108895 cites W2516208336 @default.
• W4285108895 cites W2541635437 @default.
• W4285108895 cites W2740924709 @default.
• W4285108895 cites W3015348658 @default.
• W4285108895 cites W3135550350 @default.
• W4285108895 cites W3161588687 @default.
• W4285108895 cites W4206634873 @default.
• W4285108895 cites W4243367342 @default.
• W4285108895 doi "https://doi.org/10.1007/978-3-031-05760-1_35" @default.
• W4285108895 hasPublicationYear "2022" @default.
• W4285108895 type Work @default.
• W4285108895 citedByCount "1" @default.
• W4285108895 countsByYear W42851088952023 @default.
• W4285108895 crossrefType "book-chapter" @default.
• W4285108895 hasAuthorship W4285108895A5037079840 @default.
• W4285108895 hasAuthorship W4285108895A5049729997 @default.
• W4285108895 hasAuthorship W4285108895A5081225515 @default.
• W4285108895 hasAuthorship W4285108895A5086124843 @default.
• W4285108895 hasBestOaLocation W42851088952 @default.
• W4285108895 hasConcept C119857082 @default.
• W4285108895 hasConcept C124101348 @default.
• W4285108895 hasConcept C127413603 @default.
• W4285108895 hasConcept C144133560 @default.
• W4285108895 hasConcept C154945302 @default.
• W4285108895 hasConcept C162853370 @default.
• W4285108895 hasConcept C21200559 @default.
• W4285108895 hasConcept C24326235 @default.
• W4285108895 hasConcept C41008148 @default.
• W4285108895 hasConcept C739882 @default.
• W4285108895 hasConcept C79337645 @default.
• W4285108895 hasConcept C86251818 @default.
• W4285108895 hasConceptScore W4285108895C119857082 @default.
• W4285108895 hasConceptScore W4285108895C124101348 @default.
• W4285108895 hasConceptScore W4285108895C127413603 @default.
• W4285108895 hasConceptScore W4285108895C144133560 @default.
• W4285108895 hasConceptScore W4285108895C154945302 @default.
• W4285108895 hasConceptScore W4285108895C162853370 @default.
• W4285108895 hasConceptScore W4285108895C21200559 @default.
• W4285108895 hasConceptScore W4285108895C24326235 @default.
• W4285108895 hasConceptScore W4285108895C41008148 @default.
• W4285108895 hasConceptScore W4285108895C739882 @default.
• W4285108895 hasConceptScore W4285108895C79337645 @default.
• W4285108895 hasConceptScore W4285108895C86251818 @default.
• W4285108895 hasLocation W42851088951 @default.
• W4285108895 hasLocation W42851088952 @default.
• W4285108895 hasOpenAccess W4285108895 @default.
• W4285108895 hasPrimaryLocation W42851088951 @default.
• W4285108895 hasRelatedWork W2009036560 @default.
• W4285108895 hasRelatedWork W2029968811 @default.
• W4285108895 hasRelatedWork W2230433129 @default.
• W4285108895 hasRelatedWork W2359185137 @default.
• W4285108895 hasRelatedWork W2390515779 @default.
• W4285108895 hasRelatedWork W2606848831 @default.
• W4285108895 hasRelatedWork W2998615029 @default.
• W4285108895 hasRelatedWork W3153838899 @default.
• W4285108895 hasRelatedWork W3205198673 @default.
• W4285108895 hasRelatedWork W3107369729 @default.
• W4285108895 isParatext "false" @default.
• W4285108895 isRetracted "false" @default.
• W4285108895 workType "book-chapter" @default.