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2012 IEEE 12th International Conference on Data Mining Workshops
Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification
Brussels, Belgium Belgium
December 10-December 10
ISBN: 978-1-4673-5164-5
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.
Index Terms:
Reliability,Logistics,Classification algorithms,Data mining,Algorithm design and analysis,Data models,statistical computing,data mining,high leverge point,influential observation,knowledge discovery,outlier,pattern recognition,regression,reliability
Citation:
Abdul Nurunnabi, Geoff West, "Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification," icdmw, pp.643-652, 2012 IEEE 12th International Conference on Data Mining Workshops, 2012
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