Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification
Logistic regression is well known to the data mining research community as a tool for modeling and classification. The presence of outliers is an unavoidable phenomenon in data analysis. Detection of outliers is important to increase the accuracy of the required estimates and for reliable knowledge...
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| Format: | Conference Paper |
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Conference Publishing Services
2012
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/13467 |
| _version_ | 1848748355321069568 |
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| author | Nurunnabi, Abdul West, Geoff |
| author2 | Jilles Vreeken |
| author_facet | Jilles Vreeken Nurunnabi, Abdul West, Geoff |
| author_sort | Nurunnabi, Abdul |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Logistic regression is well known to the data mining research community as a tool for modeling and classification. The presence of outliers is an unavoidable phenomenon in data analysis. Detection of outliers is important to increase the accuracy of the required estimates and for reliable knowledge discovery from the underlying databases. Most of the existing outlier detection methods in regression analysis are based on the single case deletion approach that is inefficient in the presence of multiple outliers because of the well known masking and swamping effects. To avoid these effects the multiple case deletion approach has been introduced. We propose a group deletion approach based diagnostic measure for identifying multiple influential observations in logistic regression. At the same time we introduce a plotting technique that can classify data into outliers, high leverage points, as well as influential and regular observations. This paper has two objectives. First, it investigates the problems of outlier detection in logistic regression, proposes a new method that can find multiple influential observations, and classifies the types of outlier. Secondly, it shows the necessity for proper identification of outliers and influential observations as a prelude for reliable knowledge discovery from modeling and classification via logistic regression. We demonstrate the efficiency of our method, compare the performance with the existing popular diagnostic methods, and explore the necessity of outlier detection for reliability and robustness in modeling and classification by using real datasets. |
| first_indexed | 2025-11-14T07:03:43Z |
| format | Conference Paper |
| id | curtin-20.500.11937-13467 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:03:43Z |
| publishDate | 2012 |
| publisher | Conference Publishing Services |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-134672023-02-02T07:57:38Z Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification Nurunnabi, Abdul West, Geoff Jilles Vreeken Charles Ling Mohammed J. Zaki Arno Siebes Jeffrey Xu Yu Bart Goethals Geoff Webb Xindong Wu data mining influential observation pattern recognition knowledge discovery reliability regression high leverge point statistical computing outlier Logistic regression is well known to the data mining research community as a tool for modeling and classification. The presence of outliers is an unavoidable phenomenon in data analysis. Detection of outliers is important to increase the accuracy of the required estimates and for reliable knowledge discovery from the underlying databases. Most of the existing outlier detection methods in regression analysis are based on the single case deletion approach that is inefficient in the presence of multiple outliers because of the well known masking and swamping effects. To avoid these effects the multiple case deletion approach has been introduced. We propose a group deletion approach based diagnostic measure for identifying multiple influential observations in logistic regression. At the same time we introduce a plotting technique that can classify data into outliers, high leverage points, as well as influential and regular observations. This paper has two objectives. First, it investigates the problems of outlier detection in logistic regression, proposes a new method that can find multiple influential observations, and classifies the types of outlier. Secondly, it shows the necessity for proper identification of outliers and influential observations as a prelude for reliable knowledge discovery from modeling and classification via logistic regression. We demonstrate the efficiency of our method, compare the performance with the existing popular diagnostic methods, and explore the necessity of outlier detection for reliability and robustness in modeling and classification by using real datasets. 2012 Conference Paper http://hdl.handle.net/20.500.11937/13467 10.1109/ICDMW.2012.107 Conference Publishing Services fulltext |
| spellingShingle | data mining influential observation pattern recognition knowledge discovery reliability regression high leverge point statistical computing outlier Nurunnabi, Abdul West, Geoff Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification |
| title | Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification |
| title_full | Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification |
| title_fullStr | Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification |
| title_full_unstemmed | Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification |
| title_short | Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification |
| title_sort | outlier detection in logistic regression: a quest for reliable knowledge from predictive modeling and classification |
| topic | data mining influential observation pattern recognition knowledge discovery reliability regression high leverge point statistical computing outlier |
| url | http://hdl.handle.net/20.500.11937/13467 |