Abstract
Extending previous work on quantile classifiers (q-classifiers) we propose the q*-classifier for the class imbalance problem. The classifier assigns a sample to the minority class if the minority class conditional probability exceeds 0 < q* < 1, where q* equals the unconditional probability of observing a minority class sample. The motivation for q*-classification stems from a density-based approach and leads to the useful property that the q*-classifier maximizes the sum of the true positive and true negative rates. Moreover, because the procedure can be equivalently expressed as a cost-weighted Bayes classifier, it also minimizes weighted risk. Because of this dual optimization, the q*-classifier can achieve near zero risk in imbalance problems, while simultaneously optimizing true positive and true negative rates. We use random forests to apply q*-classification. This new method which we call RFQ is shown to outperform or is competitive with existing techniques with respect to G-mean performance and variable selection. Extensions to the multiclass imbalanced setting are also considered.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 232-249 |
| Number of pages | 18 |
| Journal | Pattern Recognition |
| Volume | 90 |
| DOIs | |
| State | Published - Jun 1 2019 |
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Keywords
- Class imbalance
- Minority class
- Random forests
- Response-based sampling
- Weighted Bayes classifier
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence
Cite this
A random forests quantile classifier for class imbalanced data. / O'Brien, Robert; Ishwaran, Hemant.
In: Pattern Recognition, Vol. 90, 01.06.2019, p. 232-249.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A random forests quantile classifier for class imbalanced data
AU - O'Brien, Robert
AU - Ishwaran, Hemant
PY - 2019/6/1
Y1 - 2019/6/1
N2 - Extending previous work on quantile classifiers (q-classifiers) we propose the q*-classifier for the class imbalance problem. The classifier assigns a sample to the minority class if the minority class conditional probability exceeds 0 < q* < 1, where q* equals the unconditional probability of observing a minority class sample. The motivation for q*-classification stems from a density-based approach and leads to the useful property that the q*-classifier maximizes the sum of the true positive and true negative rates. Moreover, because the procedure can be equivalently expressed as a cost-weighted Bayes classifier, it also minimizes weighted risk. Because of this dual optimization, the q*-classifier can achieve near zero risk in imbalance problems, while simultaneously optimizing true positive and true negative rates. We use random forests to apply q*-classification. This new method which we call RFQ is shown to outperform or is competitive with existing techniques with respect to G-mean performance and variable selection. Extensions to the multiclass imbalanced setting are also considered.
AB - Extending previous work on quantile classifiers (q-classifiers) we propose the q*-classifier for the class imbalance problem. The classifier assigns a sample to the minority class if the minority class conditional probability exceeds 0 < q* < 1, where q* equals the unconditional probability of observing a minority class sample. The motivation for q*-classification stems from a density-based approach and leads to the useful property that the q*-classifier maximizes the sum of the true positive and true negative rates. Moreover, because the procedure can be equivalently expressed as a cost-weighted Bayes classifier, it also minimizes weighted risk. Because of this dual optimization, the q*-classifier can achieve near zero risk in imbalance problems, while simultaneously optimizing true positive and true negative rates. We use random forests to apply q*-classification. This new method which we call RFQ is shown to outperform or is competitive with existing techniques with respect to G-mean performance and variable selection. Extensions to the multiclass imbalanced setting are also considered.
KW - Class imbalance
KW - Minority class
KW - Random forests
KW - Response-based sampling
KW - Weighted Bayes classifier
UR - http://www.scopus.com/inward/record.url?scp=85060860214&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060860214&partnerID=8YFLogxK
U2 - 10.1016/j.patcog.2019.01.036
DO - 10.1016/j.patcog.2019.01.036
M3 - Article
AN - SCOPUS:85060860214
VL - 90
SP - 232
EP - 249
JO - Pattern Recognition
JF - Pattern Recognition
SN - 0031-3203
ER -