Dynamic class imbalance learning for incremental LPSVM

Pang, Shaoning; Zhu, Lei; Chen, Gang; Sarrafzadeh, Abdolhossein; Ban, Tao; Inoue, Daisuke

doi:10.1016/j.neunet.2013.02.007

Cited by 27 publications

(11 citation statements)

References 36 publications

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“…In a further set of experiments, we compare our proposed approaches HE_S, HE_A, HE_FUS and HE_WFUS with the best method introduced in [50], named DCIL, which was shown in that paper to outperform several other methods, such as LPSVM, SVM, SMOTE+LPSVM, weighted LPSVM, weighted SVM and partitioning ensemble SVM. In these tests, we consider the same UCI datasets used with DCIL, along with the same testing protocol (we adopt the testing protocol where the highest degree of class imbalance occurs, which is called 'Exp 3' in [50]). Each experiment consists of 20 rounds of independent tests using the F-measure as the performance indicator (we use this same measure so that our results can be directly compared with those in [50]).…”

Section: Comparisons With the Literaturementioning

confidence: 99%

“…In these tests, we consider the same UCI datasets used with DCIL, along with the same testing protocol (we adopt the testing protocol where the highest degree of class imbalance occurs, which is called 'Exp 3' in [50]). Each experiment consists of 20 rounds of independent tests using the F-measure as the performance indicator (we use this same measure so that our results can be directly compared with those in [50]).…”

Section: Comparisons With the Literaturementioning

confidence: 99%

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Coupling different methods for overcoming the class imbalance problem

2015

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Many classification problems must deal with imbalanced datasets where one class – the majority class – outnumbers the other classes. Standard classification methods do not provide accurate predictions in this setting since classification is generally biased towards the majority class. The minority classes are oftentimes the ones of interest (e.g., when they are associated with pathological conditions in patients), so methods for handling imbalanced datasets are critical. Using several different datasets, this paper evaluates the performance of state-of-the-art classification methods for handling the imbalance problem in both binary and multi-class datasets. Different strategies are considered, including the one-class and dimension reduction approaches, as well as their fusions. Moreover, some ensembles of classifiers are tested, in addition to stand-alone classifiers, to assess the effectiveness of ensembles in the presence of imbalance. Finally, a novel ensemble of ensembles is designed specifically to tackle the problem of class imbalance: the proposed ensemble does not need to be tuned separately for each dataset and outperforms all the other tested approaches. To validate our classifiers we resort to the KEEL-dataset repository, whose data partitions (training/test) are publicly available and have already been used in the open literature: as a consequence, it is possible to report a fair comparison among different approaches in the literature. Our best approach (MATLAB code and datasets not easily accessible elsewhere) will be available at https://www.dei.unipd.it/node/2357

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Section: Comparisons With the Literaturementioning

confidence: 99%

Section: Comparisons With the Literaturementioning

confidence: 99%

Coupling different methods for overcoming the class imbalance problem

2015

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“…A chunk incremental algorithm based on the cost-sensitive SVM with extended hinge loss was proposed by Gu et al [9]. Pang et al proposed an incremental wLPSVM to tackle the dynamic class imbalance problem [10]. The incremental cost-sensitive model remains an exciting and required research topic for online classification.…”

Section: Introductionmentioning

confidence: 99%

Incremental Cost-Sensitive Support Vector Machine With Linear-Exponential Loss

Zhao

Wang

et al. 2020

IEEE Access

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Incremental learning or online learning as a branch of machine learning has attracted more attention recently. For large-scale problems and dynamic data problem, incremental learning overwhelms batch learning, because of its efficient treatment for new data. However, class imbalance problem, which always appears in online classification brings a considerable challenge for incremental learning. The serious class imbalance problem may directly lead to a useless learning system. Cost-sensitive learning is an important learning paradigm for class imbalance problems and widely used in many applications. In this paper, we propose an incremental cost-sensitive learning method to tackle the class imbalance problems in the online situation. This proposed algorithm is based on a novel cost-sensitive support vector machine, which uses the Linear-exponential (LINEX) loss to implement high cost for minority class and low cost for majority class. Using the half-quadratic optimization, we first put forward the algorithm for the costsensitive support vector machine, called CSLINEX-SVM*. Then we propose the incremental cost-sensitive algorithm, ICSL-SVM. The results of numeric experiments demonstrate that the proposed incremental algorithm outperforms some conventional batch algorithms except the proposed CSLINEX-SVM*.

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“…In addition, due to the difference in number of samples belonging to the past and the new tasks, we argue that the class imbalance problem exacerbates the overfitting on the new tasks, which leads to accuracy drop. The class imbalance problem, however, is relatively less explored in the IL context [9], [38], [39]. Ditzler et al propose a dynamically-weighted consult and vote (DW-CAV) algorithm for an ensemble of classifiers for better plasticity [39].…”

Section: Introductionmentioning

confidence: 99%

Confidence Calibration for Incremental Learning

Kang

Nam

et al. 2020

IEEE Access

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Class incremental learning is an online learning paradigm wherein the classes to be recognized are gradually increased with limited memory, storing only a partial set of examples of past tasks. At a task transition, we observe an unintentional imbalance of confidence or likelihood between the classes of the past and the new task. We argue that the imbalance aggravates a catastrophic forgetting for class incremental learning. We propose a simple yet effective learning objective to balance the confidence of classes of old tasks and new task in the class incremental learning setup. In addition, we compare various sample memory configuring strategies and propose a novel sample memory management policy to alleviate the forgetting further. The proposed method outperforms the state of the arts in many evaluation metrics including accuracy and forgetting F by a large margin (up to 5.71% in A 10 and 17.1% in F 10) in extensive empirical validations on multiple visual recognition datasets such as CIFAR100, TinyImageNet and a subset of the ImageNet.

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Dynamic class imbalance learning for incremental LPSVM

Cited by 27 publications

References 36 publications

Coupling different methods for overcoming the class imbalance problem

Coupling different methods for overcoming the class imbalance problem

Incremental Cost-Sensitive Support Vector Machine With Linear-Exponential Loss

Confidence Calibration for Incremental Learning

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