Examining characteristics of predictive models with imbalanced big data

Abstract

Abstract High class imbalance between majority and minority classes in datasets can skew the performance of Machine Learning algorithms and bias predictions in favor of the majority (negative) class. This bias, for cases where the minority (positive) class is of greater interest and the occurrence of false negatives is costlier than false positives, may result in adverse consequences. Our paper presents two case studies, each utilizing a unique, combined approach of Random Undersampling and Feature Selection to investigate the effect of class imbalance on big data analytics. Random Undersampling is used to generate six class distributions ranging from balanced to moderately imbalanced, and Feature Importance is used as our Feature Selection method. Classification performance was reported for the Random Forest, Gradient-Boosted Trees, and Logistic Regression learners, as implemented within the Apache Spark framework. The first case study utilized a training dataset and a test dataset from the ECBDL’14 bioinformatics competition. The training and test datasets contain about 32 million instances and 2.9 million instances, respectively. For the first case study, Gradient-Boosted Trees obtained the best results, with either a features-set of 60 or the full set, and a negative-to-positive ratio of either 45:55 or 40:60. The second case study, unlike the first, included training data from one source (POST dataset) and test data from a separate source (Slowloris dataset), where POST and Slowloris are two types of Denial of Service attacks. The POST dataset contains about 1.7 million instances, while the Slowloris dataset contains about 0.2 million instances. For the second case study, Logistic Regression obtained the best results, with a features-set of 5 and any of the following negative-to-positive ratios: 40:60, 45:55, 50:50, 65:35, and 75:25. We conclude that combining Feature Selection with Random Undersampling improves the classification performance of learners with imbalanced big data from different application domains.

Keywords

• Big data
• Feature Importance
• Feature Selection
• Class Imbalance
• Machine Learning
• Random Undersampling