PBC4cip: A new contrast pattern-based classifier for class imbalance problems

Pattern-Based and Visual Analytics for Visitor Analysis on Websites

“…A contrast pattern is a pattern appearing significantly in a class regarding to the other classes [9,[34][35][36]. These patterns describe common characteristics of objects in a class.…”

“…We propose to define the classes based on the segments of interest. Contrast pattern-based classifiers have been used on several real-world applications like characterization for subtypes of leukemia, classification of spatial and image data, structural alerts for computational toxicology and prediction of heart diseases; among others, where they have shown good classification results [9,[36][37][38][39][40].…”

“…For each decision tree, patterns are extracted from the paths from the root node to the leaves. For each extracted pattern, the class with the highest support determines the class of the pattern [36,40,41].…”

“…Also, Random Forest miner have allowed obtaining better classification results when Hellinger distance [42] is used for evaluating each binary candidate split at each decision tree level than using the information gain measure [43]. This result is due to the Hellinger distance is unaffected by the class imbalance problem because it rewards, in a better way than the information gain measure [36].…”

“…For all of the above reasons, in this paper, we have selected the Random Forest miner jointly with the Hellinger distance as the algorithm for mining contrast patterns. Also, we used the pattern filtering method introduced in [36] for obtaining a small collection of high-quality pattern describing the problem's classes, in this case the segments of interest. These patterns can guide the analysis of the data, by pointing to interesting visitor patterns; guiding the user towards creating queries to filter the data, instead of guessing interesting subsets of visitors that may appear in the data.…”