论文信息 - Outlier Diagnostics in Logistic Regression: A Supervised Learning Technique

Outlier Diagnostics in Logistic Regression: A Supervised Learning Technique

The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. Logistic regression is one of the most popular supervised learning technique that is used in classification. Fields like computer vision, image analysis and engineering sciences frequently encounter data with outliers (noise). Presence of outliers in the training sample may be the cause of large training time, misclassification, and to design a faulty classifier. This article provides a new method for identifying outliers in logistic regression. The significance of the measure is shown by well-referred data sets.

Abdul Nurunnabi | Mohammed Nasser | M. Nasser | A. Nurunnabi

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