A new weighted approach to imbalanced data classification problem via support vector machine with quadratic cost function

Hwang, Jae Pil; Park, Seongkeun; Kim, Euntai

doi:10.1016/j.eswa.2011.01.061

Cited by 72 publications

(45 citation statements)

References 23 publications

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“…( , ) = (− ‖ − ‖ 2 ), ≥ 0 (2) For each class, the weights are estimated as the inverse of the class size: + = + , − = − (3) By referring to previous works [4][5][6][7], where + and − are the size of normal and abnormal class, + and − are weights corresponding to the normal and abnormal classes respectively. Note that for balanced problems the weights become equal ( + = − ) and the algorithm reduces to SVM.…”

Section: Resultsmentioning

confidence: 99%

An Effective and Novel Weighted Support Vector Machine Method for Control Chart Pattern Recognition

Chen¹,

Xia²,

Chen³

2016

Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications

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Abstract. Control chart pattern recognition is the method to realize quality online monitoring and diagnosis of production process. For the conditions that the number of existing normal mode products is much higher than the abnormal ones during the actual manufacturing process, we proposed a method about WSVM (Weighted Support Vector Machines) for dynamic process of abnormal pattern recognition based on PCA (Principal Component Analysis). We put the proposed method into our experiment, the experimental simulation results show that this method proposed in this paper has a big advantage over the existing SVM (Support Vector Machine) on highly imbalanced classification problem, which suitable for quality monitoring and diagnosis of dynamic production process.

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Section: Resultsmentioning

confidence: 99%

An Effective and Novel Weighted Support Vector Machine Method for Control Chart Pattern Recognition

Chen¹,

Xia²,

Chen³

2016

Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications

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“…Some classification accuracies may not be the reliable indicators, particularly if the training data are imbalanced [41,77]. As single-class data description is a special type of one-class classification, there are difficulties that may exist when trying to fit a single-class learner using the positive samples only.…”

Section: Scoring Modelmentioning

confidence: 99%

Single-Class Data Descriptors for Mapping Panax notoginseng through P-Learning

Deng

2018

Applied Sciences

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Machine learning-based remote-sensing techniques have been widely used for the production of specific land cover maps at a fine scale. P-learning is a collection of machine learning techniques for training the class descriptors on the positive samples only. Panax notoginseng is a rare medicinal plant, which also has been a highly regarded traditional Chinese medicine resource in China for hundreds of years. Until now, Panax notoginseng has scarcely been observed and monitored from space. Remote sensing of natural resources provides us new insights into the resource inventory of Chinese materia medica resources, particularly of Panax notoginseng. Generally, land-cover mapping involves focusing on a number of landscape classes. However, sometimes a subset or one of the classes will be the only part of interest. In term of this study, the Panax notoginseng field is the right unit class. Such a situation makes single-class data descriptors (SCDDs) especially significant for specific land-cover interpretation. In this paper, we delineated the application such that a stack of SCDDs were trained for remote-sensing mapping of Panax notoginseng fields through P-learning. We employed and compared SCDDs, i.e., the simple Gaussian target distribution, the robust Gaussian target distribution, the minimum covariance determinant Gaussian, the mixture of Gaussian, the auto-encoder neural network, the k-means clustering, the self-organizing map, the minimum spanning tree, the k-nearest neighbor, the incremental support vector data description, the Parzen density estimator, and the principal component analysis; as well as three ensemble classifiers, i.e., the mean, median, and voting combiners. Experiments demonstrate that most SCDDs could achieve promising classification performance. Furthermore, this work utilized a set of the elaborate samples manually collected at a pixel-level by experts, which was intended to be a benchmark dataset for the future work. The measuring performance of SCDDs gives us challenging insights to define the selection criteria and scoring proof for choosing a fine SCDD in mapping a specific landscape class. With the increment of remotely sensed satellite data of the study area, the spatial distribution of Panax notoginseng could be continuously derived in the local area on the basis of SCDDs.

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“…However, in spite of slight improvement by using sampling methods, using such methods results in an increased computational cost due to the increase in the training data points (Hwang et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Hwang et al (Hwang et al, 2011) simply set the weight according to the size of positive and negative dataset.…”

Section: Introductionmentioning

confidence: 99%

Smarter Classification for Imbalanced Data Set and Its Application to Patent Evaluation

Kwon¹,

Lee²

2014

Journal of Intelligence and Information Systems

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․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․Overall, accuracy as a performance measure does not fully consider modular accuracy: the accuracy of classifying 1 (or true) as 1 is not same as classifying 0 (or false) as 0. A smarter classification algorithm would optimize the classification rules to match the modular accuracies' goals according to the nature of problem. Correspondingly, smarter algorithms must be both more generalized with respect to the nature of problems, and free from decretization, which may cause distortion of the real performance. Hence, in this paper, we propose a novel vertical boosting algorithm that improves modular accuracies. Rather than decretizing items, we use simple classifiers such as a regression model that accepts continuous data types. To improve the generalization, and to select a classification model that is well-suited to the nature of the problem domain, we developed a model selection algorithm with smartness. To show the soundness of the proposed method, we performed an experiment with a real-world application: predicting the intellectual properties of e-transaction technology, which had a 47,000+ record data set. ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․

show abstract

A new weighted approach to imbalanced data classification problem via support vector machine with quadratic cost function

Cited by 72 publications

References 23 publications

An Effective and Novel Weighted Support Vector Machine Method for Control Chart Pattern Recognition

An Effective and Novel Weighted Support Vector Machine Method for Control Chart Pattern Recognition

Single-Class Data Descriptors for Mapping Panax notoginseng through P-Learning

Smarter Classification for Imbalanced Data Set and Its Application to Patent Evaluation

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