Cost-sensitive decision tree ensembles for effective imbalanced classification

By Bartosz Krawczyk, Michal Wozniak, Gerald Schaefer. Applied Soft Computing, 2014

361 citations recorded by the source corpus.

Real-life datasets are often imbalanced, that is, there are significantly more training samples available for some classes than for others, and consequently the conventional aim of reducing overall classification accuracy is not appropriate when dealing with such problems. Various approaches have been introduced in the literature to deal with imbalanced datasets, and are typically based on oversampling, undersampling or cost-sensitive classification. In this paper, we introduce an effective ensemble of cost-sensitive decision trees for imbalanced classification. Base classifiers are constructed according to a given cost matrix, but are trained on random feature subspaces to ensure sufficient diversity of the ensemble members. We employ an evolutionary algorithm for simultaneous classifier selection and assignment of committee member weights for the fusion process. Our proposed algorithm is evaluated on a variety of benchmark datasets, and is confirmed to lead to improved recognition of the minority class, to be capable of outperforming other state-of-the-art algorithms, and hence to represent a useful and effective approach for dealing with imbalanced datasets.

Subfields: Accounting, Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/fe6e3b85113f09b488cb5b8fd52a0a94e47e89ba

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Citations in the corpus

Learning from imbalanced data: open challenges and future directions (2016, background): “Merging data-level solutions with classifier ensembles [64], resulting in robust and efficient learners [34] is highly popular.”
A Systematic Review on Imbalanced Data Challenges in Machine Learning (2019, background): “Krawczyk et al. (2014) proposed a cost sensitive decision trees for the imbalanced data classification, since real datasets are mostly imbalanced.”
Addressing imbalance in multilabel classification: Measures and random resampling algorithms (2015, background): “Generally, the imbalance problem has been faced with three different approaches [6]: data resampling, algorithmic adaptations [5], and cost sensitive classification [20].”
An embedded feature selection method for imbalanced data classification (2019, methodology): “[4] propose a noisefiltered undersampling scheme to eliminate the noisy minority data before executing resampling method for imbalanced classification.”