Revisiting Data Complexity Metrics Based on Morphology for Overlap and
Imbalance: Snapshot, New Overlap Number of Balls Metrics and Singular
Problems Prospect
Data Science and Machine Learning have become fundamental assets for
companies and research institutions alike. As one of its fields, supervised
classification allows for class prediction of new samples, learning from given
training data. However, some properties can cause datasets to be problematic to
classify.
In order to evaluate a dataset a priori, data complexity metrics have been
used extensively. They provide information regarding different intrinsic
characteristics of the data, which serve to evaluate classifier compatibility
and a course of action that improves performance. However, most complexity
metrics focus on just one characteristic of the data, which can be insufficient
to properly evaluate the dataset towards the classifiers' performance. In fact,
class overlap, a very detrimental feature for the classification process
(especially when imbalance among class labels is also present) is hard to
assess.
This research work focuses on revisiting complexity metrics based on data
morphology. In accordance to their nature, the premise is that they provide
both good estimates for class overlap, and great correlations with the
classification performance. For that purpose, a novel family of metrics have
been developed. Being based on ball coverage by classes, they are named after
Overlap Number of Balls. Finally, some prospects for the adaptation of the
former family of metrics to singular (more complex) problems are discussed.Comment: 23 pages, 9 figures, preprin