Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386493170> ?p ?o ?g. }
Showing items 1 to 51 of
51
with 100 items per page.
- W4386493170 abstract "Abstract Anomaly detection is a fundamental task in the field of unsupervised machine learning, aimed at identifying instances that significantly deviate from other input data. This problem has various applications, including identifying defective products in industries, detecting network intrusions, medical diagnostics, and many other cases. Despite extensive research conducted in this field, a solution with satisfactory performance under all conditions and types of data has not yet been achieved. One effective unsupervised method is the Random Histogram Forest (RHF) approach, which utilizes a probabilistic approach. Based on the evaluation metric of average precision of the area under the precision-recall curve (AP), this approach has shown better performance compared to other methods in terms of AP. However, due to its inherent mechanism, this approach also has limitations. In this article, these limitations are examined, and solutions and methods are proposed to address these constraints, and an extension approach called Extended Random Histogram Forest (ERHF) is introduced for unsupervised anomaly detection. ERHF utilizes data projection and dimensionality reduction methods to enhance the compatibility between the data and the internal mechanism of RHF. Instead of randomly selecting splitting points in the process of constructing random trees, the Skewness score is used to choose more targeted splitting points. Additionally, multidimensional hyperplanes with random slopes are used for data partitioning instead of pointwise splits. Furthermore, the mechanism for calculating the anomaly score of instances is modified to enable the use of subsampling. The performance evaluation of the proposed ERHF method using various metrics (AP, AUC, Recall, F1) on ODDS datasets demonstrates that ERHF significantly outperforms the RHF method." @default.
- W4386493170 created "2023-09-07" @default.
- W4386493170 creator A5022739916 @default.
- W4386493170 creator A5092825466 @default.
- W4386493170 date "2023-09-06" @default.
- W4386493170 modified "2023-10-16" @default.
- W4386493170 title "Extended Random Histogram Forest for Unsupervised Anomaly Detection" @default.
- W4386493170 doi "https://doi.org/10.21203/rs.3.rs-3321436/v1" @default.
- W4386493170 hasPublicationYear "2023" @default.
- W4386493170 type Work @default.
- W4386493170 citedByCount "0" @default.
- W4386493170 crossrefType "posted-content" @default.
- W4386493170 hasAuthorship W4386493170A5022739916 @default.
- W4386493170 hasAuthorship W4386493170A5092825466 @default.
- W4386493170 hasBestOaLocation W43864931701 @default.
- W4386493170 hasConcept C115961682 @default.
- W4386493170 hasConcept C119857082 @default.
- W4386493170 hasConcept C124101348 @default.
- W4386493170 hasConcept C153180895 @default.
- W4386493170 hasConcept C154945302 @default.
- W4386493170 hasConcept C169258074 @default.
- W4386493170 hasConcept C2777036070 @default.
- W4386493170 hasConcept C41008148 @default.
- W4386493170 hasConcept C53533937 @default.
- W4386493170 hasConcept C739882 @default.
- W4386493170 hasConceptScore W4386493170C115961682 @default.
- W4386493170 hasConceptScore W4386493170C119857082 @default.
- W4386493170 hasConceptScore W4386493170C124101348 @default.
- W4386493170 hasConceptScore W4386493170C153180895 @default.
- W4386493170 hasConceptScore W4386493170C154945302 @default.
- W4386493170 hasConceptScore W4386493170C169258074 @default.
- W4386493170 hasConceptScore W4386493170C2777036070 @default.
- W4386493170 hasConceptScore W4386493170C41008148 @default.
- W4386493170 hasConceptScore W4386493170C53533937 @default.
- W4386493170 hasConceptScore W4386493170C739882 @default.
- W4386493170 hasLocation W43864931701 @default.
- W4386493170 hasOpenAccess W4386493170 @default.
- W4386493170 hasPrimaryLocation W43864931701 @default.
- W4386493170 hasRelatedWork W1498259939 @default.
- W4386493170 hasRelatedWork W1983610137 @default.
- W4386493170 hasRelatedWork W2076520961 @default.
- W4386493170 hasRelatedWork W2097458023 @default.
- W4386493170 hasRelatedWork W2911455822 @default.
- W4386493170 hasRelatedWork W2964383635 @default.
- W4386493170 hasRelatedWork W2990472155 @default.
- W4386493170 hasRelatedWork W3174196512 @default.
- W4386493170 hasRelatedWork W4308191010 @default.
- W4386493170 hasRelatedWork W4323021782 @default.
- W4386493170 isParatext "false" @default.
- W4386493170 isRetracted "false" @default.
- W4386493170 workType "article" @default.