Paper ID: 2409.13007 • Published Sep 19, 2024
iCost: A Novel Instance Complexity Based Cost-Sensitive Learning Framework
Asif Newaz, Asif Ur Rahman Adib, Taskeed Jabid
TL;DR
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Class imbalance in data presents significant challenges for classification
tasks. It is fairly common and requires careful handling to obtain desirable
performance. Traditional classification algorithms become biased toward the
majority class. One way to alleviate the scenario is to make the classifiers
cost-sensitive. This is achieved by assigning a higher misclassification cost
to minority-class instances. One issue with this implementation is that all the
minority-class instances are treated equally, and assigned with the same
penalty value. However, the learning difficulties of all the instances are not
the same. Instances that are located in the overlapping region or near the
decision boundary are harder to classify, whereas those further away are
easier. Without taking into consideration the instance complexity and naively
weighting all the minority-class samples uniformly, results in an unwarranted
bias and consequently, a higher number of misclassifications of the
majority-class instances. This is undesirable and to overcome the situation, we
propose a novel instance complexity-based cost-sensitive approach (termed
'iCost') in this study. We first categorize all the minority-class instances
based on their difficulty level and then the instances are penalized
accordingly. This ensures a more equitable instance weighting and prevents
excessive penalization. The performance of the proposed approach is tested on
65 binary and 10 multiclass imbalanced datasets against the traditional
cost-sensitive learning frameworks. A significant improvement in performance
has been observed, demonstrating the effectiveness of the proposed strategy.