engleski

Supervised and Unsupervised Machine Learning Approaches on Class Imbalanced Data

Huge amounts of data are stored digitally every day. This data has various characteristics. Class imbalance is one of the characteristics that has the effect of machine learning algorithms performance and this problem is receiving attention among academia and industry. Class imbalance occurs when the number of instances in one class is significantly different than the number of instances in the other class (in binary classification). In this paper, we are combining supervised and unsupervised machine learning approaches on one imbalanced dataset from a publicly available repository. Unsupervised machine learning approach of cluster analysis is applied on the most significant variables discovered by sensitivity analysis on predictive models developed by decision tree. Our results indicated a hybrid approach of decision tree and cluster analysis as a promising tool to work with imbalanced data.

class imbalance ; cluster analysis ; decision tree ; machine learning.

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