Evolutionary-based selection of generalized instances for imbalanced classification

García, Salvador; Derrac, Joaquín; Triguero, Isaac; Carmona, C. J.; Herrera, F.

doi:10.1016/j.knosys.2011.01.012

Cited by 126 publications

(41 citation statements)

References 38 publications

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“…The proposed technique follows the same baseline while leveraging the disjuncts and generalization issue. Evolutionary algorithms resolve the imbalanced Big Data sets issue using the technique belonging to nested generalized model, considering objects in Euclidean n-space [23]. Boundary based oversampling technique used in SMOTE + GLMBoost and NRBoundary-SMOTE [24] are engaged to resolve imbalance data set problems.…”

Section: Related Workmentioning

confidence: 99%

Improved classification of large imbalanced data sets using rationalized technique: Updated Class Purity Maximization Over_Sampling Technique (UCPMOT)

Patil¹,

Sonavane²

2017

J Big Data

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Section: Related Workmentioning

confidence: 99%

Improved classification of large imbalanced data sets using rationalized technique: Updated Class Purity Maximization Over_Sampling Technique (UCPMOT)

Patil¹,

Sonavane²

2017

J Big Data

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“…To do this, we rely on the success of oversampling approaches in imbalanced domains [48], [49], [50], [51], but with the difference that we deal with all the classes of the problem.…”

Section: A Motivation: Why Add Synthetic Examples?mentioning

confidence: 99%

“…In the specialized literature, this kind of techniques, such as [23], [50], does not provide any theoretical analyses because of the stochastic nature of the models. However, their applicability and effectiveness has been proved in many real world applications [52], [53].…”

Section: Self-labeling With Synthetic Datamentioning

confidence: 99%

SEG-SSC: A Framework Based on Synthetic Examples Generation for Self-Labeled Semi-Supervised Classification

Triguero

García

Herrera

2015

IEEE Trans. Cybern.

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Abstract-Self-labeled techniques are semi-supervised classification methods that address the shortage of labeled examples via a self-learning process based on supervised models. They progressively classify unlabeled data and use them to modify the hypothesis learned from labeled samples. Most relevant proposals are currently inspired by boosting schemes to iteratively enlarge the labeled set. Despite their effectiveness, these methods are constrained by the number of labeled examples and their distribution, which in many cases is sparse and scattered. The aim of this work is to design a framework, named SEG-SSC, to improve the classification performance of any given self-labeled method by using synthetic labeled data. These are generated via an oversampling technique and a positioning adjustment model that use both labeled and unlabeled examples as reference. Next, these examples are incorporated in the main stages of the self-labeling process. The principal aspects of the proposed framework are: (a) introducing diversity to the multiple classifiers used by using more (new) labeled data, (b) fulfilling labeled data distribution with the aid of unlabeled data, and (c) being applicable to any kind of self-labeled method. In our empirical studies, we have applied this scheme to four recent self-labeled methods, testing their capabilities with a large number of data sets. We show that this framework significantly improves the classification capabilities of self-labeled techniques.

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“…They have given the suggestion for applying the random forest and gradient boosting classifiers for better performance. Salvador Garcı´aet al [9] have used evolutionary technique to solve the class imbalance problem. They proposed a method belonging to the family of the nested generalized exemplar that accomplishes learning by storing objects in Euclidean n-space.…”

Section: Literature Survey On Imbalance Datasetsmentioning

confidence: 99%

A Novel Class Imbalance Learning using Ordering Points Clustering

Rao¹,

Rao²,

Lakshmi³

2012

IJCA

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In Data mining and Knowledge Discovery hidden and valuable knowledge from the data sources is discovered. The traditional algorithms used for knowledge discovery are bottle necked due to wide range of data sources availability. Class imbalance is a one of the problem arises due to data source which provide unequal class i.e. examples of one class in a training data set vastly outnumber examples of the other class(es).This paper proposes a method belonging to under sampling approach which uses OPTICS one of the best visualization clustering technique for handling class imbalance problem. In the proposed approach, further Classification of new data is performed by applying C4.5 algorithm as the base algorithm. The method is optimized by the selection of the most suitable clusters for deletion of the majority dataset based on visualization algorithms. An experimental analysis is carried out over a wide range of highly imbalanced data sets and uses the statistical tests suggested in the specialized literature. The results obtained show that our novel proposal outperforms other classic and recent models in terms of Area under the ROC Curve, F-measure, precision, TP rate and TN rate.

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Evolutionary-based selection of generalized instances for imbalanced classification

Cited by 126 publications

References 38 publications

Improved classification of large imbalanced data sets using rationalized technique: Updated Class Purity Maximization Over_Sampling Technique (UCPMOT)

Improved classification of large imbalanced data sets using rationalized technique: Updated Class Purity Maximization Over_Sampling Technique (UCPMOT)

SEG-SSC: A Framework Based on Synthetic Examples Generation for Self-Labeled Semi-Supervised Classification

A Novel Class Imbalance Learning using Ordering Points Clustering

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