Optimizing F-measure with non-convex loss and sparse linear classifiers

Chinta, Punya Murthy; Balamurugan, P.; Shevade, Shirish; Murty, M. Narasimha

doi:10.1109/ijcnn.2013.6707105

Cited by 9 publications

(11 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Optimizing the F-measure is another popular method for imbalance learning. Joachims [15], Chinta et al [20], Maratea et al [21], and Lipton et al [22] used different approximates to the F-measure and designed different classifies. Numerical experiments on the benchmark datasets demonstrated their algorithms' effectiveness.…”

Section: Relevant Backgroundmentioning

confidence: 99%

“…The second group algorithm-oriented methods aim at the extension and modification of existing classification algorithms so that they can be more effective in dealing with imbalanced data. For example, Liu et al and Kang and Ramamohanarao have presented two different modified decision tree algorithms for improving the standard C4.5, such as CCPDT [11] and HeDEx [12], while Köknar-Tezel et al, Joachims et al, and Lipton et al have proposed various approaches to improve traditional SVM's performance on the imbalanced settings [13][14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

“…However, since, in this paper, our goal is to improve the existing statistical learning algorithm, in the following we are interested in algorithm-oriented method and propose a modified SVM approach by directly optimizing imbalance measure. It seems that our algorithm is similar to the algorithms in [15][16][17][18][19][20][21][22]; however, we design different objective functions and use different optimization techniques with theirs. More specifically, this paper makes the following contributions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Structural SVM Based Approach for Binary Classification under Class Imbalance

Cheng

Yang

Zhang

2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Class imbalance situations, where one class is rare compared to the other, arise frequently in machine learning applications. It is well known that the usual misclassification error is not suitable in such settings. A wide range of performance measures such as AM and QM have been proposed for this problem. However, due to computational difficulties, few learning techniques have been developed to directly optimize for AM or QM metric. To fill the gap, in this paper, we present a general structural SVM framework for directly optimizing AM and QM. We define the loss functions oriented to AM and QM, respectively, and adopt the cutting plane algorithm to solve the outer optimization. For the inner problem of finding the most violated constraint, we propose two efficient algorithms for the AM and QM problem. Empirical studies on the various imbalanced datasets justify the effectiveness of the proposed approach.

show abstract

Section: Relevant Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Structural SVM Based Approach for Binary Classification under Class Imbalance

Cheng

Yang

Zhang

2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Therefore, various approximation algorithms have been proposed, which mainly fall into two paradigms [8]. The Empirical Utility Maximization (EUM) approach learns a classifier having optimal performance on training data [9][10][11][12][13][14][15][16], while the decision-theoretic (DT) approach learns a probabilistic model and then predicts labels with maximum expected -measure [17][18][19][20]. Since, in this paper, our aim is to design an efficient classifier for maximizing -measure, and DT approach possibly needs high computational complexity for the prediction step [8], in the following, we are focused on the Empirical Utility Maximization approach.…”

Section: Introductionmentioning

confidence: 99%

“…As the -measure is a nonconvex metric, EUM approach often designs convex surrogates for optimizing -measure and results in the development of two types of methods. The first type belongs to the "direct method," which directly defines different surrogate objective functions for maximizing -measure [9][10][11][12][13][14][15]. One representative work is SVMperf, which adopts structural SVM as surrogate framework, and uses cutting plane algorithm to solve the inner optimization [11].…”

Section: Introductionmentioning

confidence: 99%

Efficient Optimization ofF-Measure with Cost-Sensitive SVM

Cheng

Zhou

Gao

et al. 2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

F-measure is one of the most commonly used performance metrics in classification, particularly when the classes are highly imbalanced. Direct optimization of this measure is often challenging, since no closed form solution exists. Current algorithms design the classifiers by using the approximations to theF-measure. These algorithms are not efficient and do not scale well to the large datasets. To fill the gap, in this paper, we propose a novel algorithm, which can efficiently optimizeF-measure with cost-sensitive SVM. First of all, we present an explicit transformation from the optimization ofF-measure to cost-sensitive SVM. Then we adopt bundle method to solve the inner optimization. For the problem where the existing bundle method may have the fluctuations in the primal objective during iterations, an additional line search procedure is involved, which can alleviate the fluctuations problem and make our algorithm more efficient. Empirical studies on the large-scale datasets demonstrate that our algorithm can provide significant speedups over current state-of-the-artF-measure based learners, while obtaining better (or comparable) precise solutions.

show abstract