Revisiting Example Dependent Cost-Sensitive Learning with Decision Trees

Aodha, Oisin Mac; Brostow, Gabriel J.

doi:10.1109/iccv.2013.31

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…The RUSBoost method has been shown to provide a good solution to this problem in our work, which utilizes a sampling technique to handle class imbalance. Other recent approaches ( Mac Aodha and Brostow, 2013 , Bahnsen et al, 2015 ) may provide an alternative way for handling the class imbalance problem. Furthermore, in this work, different types of features were concatenated for classification.…”

Section: Discussionmentioning

confidence: 99%

Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting

Tong

Ledig

Guerrero

et al. 2017

NeuroImage: Clinical

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Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making.

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Section: Discussionmentioning

confidence: 99%

Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting

Tong

Ledig

Guerrero

et al. 2017

NeuroImage: Clinical

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“…So, for example, the cost of misclassifying a process plant as safe is likely to be much higher than the cost of misclassifying a safe plant as unsafe. The past three decades have seen a significant interest in this problem, known as cost-sensitive induction, with the development of a number of independent algorithms [8,19,22,34,35,36,37,38,62,63]. Authors have also recognised that in practice there are other costs involved [5,7,8,9].…”

Section: Cost-sensitive Decision Tree Learningmentioning

confidence: 99%

A Cost-Sensitive Decision Tree Learning Algorithm Based on a Multi-Armed Bandit Framework

Lomax

Vadera

2016

The Computer Journal

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This paper develops a new algorithm for inducing cost-sensitive decision trees that is inspired by the multi-armed bandit problem, in which a player in a casino has to decide which slot machine (bandit) from a selection of slot machines is likely to pay out the most. Game Theory proposes a solution to this multi-armed bandit problem by using a process of exploration and exploitation in which reward is maximized. This paper utilizes these concepts to develop a new algorithm by viewing the rewards as a reduction in costs, and utilizing the exploration and exploitation techniques so that a compromise between decisions based on accuracy and decisions based on costs can be found. The algorithm employs the notion of lever pulls in the multi-armed bandit game to select the attributes during decision tree induction, using a lookahead methodology to explore potential attributes and exploit the attributes which maximizes the reward. The new algorithm is evaluated on fifteen datasets and compared to six wellknown algorithms J48, EG2, MetaCost, AdaCostM1, ICET and ACT. The results obtained show that the new multi-armed based algorithm can produce more cost-effective trees without compromising accuracy. The paper also includes a critical appraisal of the limitations of the new algorithm and proposes avenues for further research.

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“…Lastly, the base classifiers are combined using either majority voting, cost-sensitive weighted voting and costsensitive stacking. Majority voting consists in collecting the predictions of each base classifier and selecting the decision with the highest number of votes, see (7).…”

Section: Algorithmsmentioning

confidence: 99%

“…Also, none of these methods uses take into account the cost of correct classification. Moreover, the literature on example-dependent costsensitive methods is limited, often because there is a lack of publicly available datasets that fit the problem [7]. Recently, we have proposed different methods that take into account the different example-dependent costs, in particular: Bayes minimum risk (BM R) [8], cost-sensitive logistic regression [9], and cost-sensitive decision tree (CSDT ) [10].…”

Section: Introductionmentioning

confidence: 99%