An Alternative Artificial Intelligence Technique for Detecting Outliers
Hegazy Zaher, Abd El-Fattah Kandil, Rehab Shehata
Journal of Advances in Mathematics and Computer Science · pp. 2799–2810 · Published 23 Jul 2014
10.9734/BJMCS/2014/11194Abstract
Data are rarely perfect. Whether the problem is data entry errors or rare events. Outliers have two opposing properties. They can be noises that disturb regression and classification task. On the other hand, they can provide valuable information about rare phenomena, which can lead to knowledge discovery. This paper proposes a hybrid algorithm including K Nearest Neighbor and Support Vector Machine (KSVM) that detects outliers by taking the advantages of the two intelligent techniques, Support Vector Machine (SVM) and K Nearest Neighbour (KNN). Also a global efficiency measure introduced to compare different methods. Finally, a comparison between KNN, SVM, and KSVM is conducted using detection rate, accuracy rate, false alarm rate, true negative rate and the proposed global efficiency measure based on benchmark data called Milk data.
Cited by 0
No indexed citations yet.
Related research
- Classification of Ear Biometric Data using Support Vector Machine — shares topic coverage
- Analysis of Spatially Distributed Monthly Rainfall and Temperature over Bangladesh from 1989 to 2019 — shares topic coverage
- Comparison of Crop Area Estimation for the Winter Season Using the Google Earth Engine (GEE) Platform and Traditional Method of Supervisor Classification — shares topic coverage
- Daily Sediment Yield Prediction Using Hybrid Machine Learning Approach — shares topic coverage
- A Comparative Machine Learning Framework for Location-Specific Maize Yield Prediction in the U.S. Corn Belt Using SVM, Random Forest, and Decision Tree Models: Integrating Agronomic, Genetic, and Spatial Factors — shares topic coverage
Article metrics
Real usage data collected on this platform.
5
Page views
0
PDF downloads
0
Outbound clicks
0
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.