IFROWANN: Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification

Ramentol, Enislay; Vluymans, Sarah; Verbiest, Nele; Caballero, Yailé; Bello, Rafael; Cornelis, Chris; Herrera, Francisco

doi:10.1109/tfuzz.2014.2371472

Cited by 92 publications

(47 citation statements)

References 47 publications

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“…GM = TP rate · TN rate (1) Specific methods must be applied so that traditional classifiers are able to deal with the imbalance between classes. Three different methodologies are traditionally followed to cope with this problem [3,13]: data level solutions that rebalance the training set [10], algorithmic level solutions that adapt the learning stage towards the minority classes [11] and cost-sensitive solutions which consider different costs with respect to the class distribution [12]. It is also possible to combine several classifiers into ensembles [26], by modifying or adapting the combination of the algorithm itself of ensemble learning and any of the techniques described above, namely at the data level or by algorithmic approaches based on cost-sensitive learning [27].…”

Section: Classification With Imbalanced Datasetsmentioning

confidence: 99%

“…The first is the family of pre-processing techniques aiming to rebalance the training data [10]. The second one is related to the algorithmic approaches that alter the learning mechanism by taking into account the different class distribution [11]. The third category comprises cost-sensitive learning approaches that consider a different cost for the misclassification of each class [12,13].…”

Section: Introductionmentioning

confidence: 99%

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An insight into imbalanced Big Data classification: outcomes and challenges

Fernández

Río

Chawla

et al. 2017

Complex Intell. Syst.

Self Cite

187

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Big Data applications are emerging during the last years, and researchers from many disciplines are aware of the high advantages related to the knowledge extraction from this type of problem. However, traditional learning approaches cannot be directly applied due to scalability issues. To overcome this issue, the MapReduce framework has arisen as a "de facto" solution. Basically, it carries out a "divide-andconquer" distributed procedure in a fault-tolerant way to adapt for commodity hardware. Being still a recent discipline, few research has been conducted on imbalanced classification for Big Data. The reasons behind this are mainly the difficulties in adapting standard techniques to the MapReduce programming style. Additionally, inner problems of imbalanced data, namely lack of data and small disjuncts, are accentuated during the data partitioning to fit the MapReduce programming style. This paper is designed under three main pillars. First, to present the first outcomes for imbalanced classification in Big Data problems, introducing the current

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Section: Classification With Imbalanced Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An insight into imbalanced Big Data classification: outcomes and challenges

Fernández

Río

Chawla

et al. 2017

Complex Intell. Syst.

Self Cite

187

View full text Add to dashboard Cite

show abstract

“…Huang and Kuo [159] investigated two perspectives of cross-lingual semantic document similarity measures based on the fuzzy sets and rough sets which were named formulation of similarity measures and document representation. Ramentol et al [161] developed a learning algorithm for considering the imbalance representation and proposed a classification algorithm for imbalanced data by using the fuzzy-rough sets and ordered weighted average aggregation. Derrac et al [163] introduced a new fuzzyrough set model for prototype selection by optimising the behaviour of this classifier.…”

Section: Distribution Of Papers Based On Other Application Areasmentioning

confidence: 99%

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

et al. 2017

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Rough set theory has been used extensively in fields of complexity, cognitive sciences, and artificial intelligence, especially in numerous fields such as expert systems, knowledge discovery, information system, inductive reasoning, intelligent systems, data mining, pattern recognition, decision-making, and machine learning. Rough sets models, which have been recently proposed, are developed applying the different fuzzy generalisations. Currently, there is not a systematic literature review and classification of these new generalisations about rough set models. Therefore, in this review study, the attempt is made to provide a comprehensive systematic review of methodologies and applications of recent generalisations discussed in the area of fuzzy-rough set theory. On this subject, the Web of Science database has been chosen to select the relevant papers. Accordingly, the systematic and metaanalysis approach, which is called "PRISMA," has been proposed and the selected articles were classified based on the author and year of publication, author nationalities, application field, type of study, study category, study contribution, and journal in which the articles have appeared. Based on the results of this review, we found that there are many challenging issues related to the different application area of fuzzy-rough set theory which can motivate future research studies.

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“…al. [21] proposed a fuzzy rough ordered weighted average nearest neighbor method for binary classification with six weight vectors blended with some indiscernibility relations. Fernandez et al [22] analyzed the fuzzy rule based classification systems for imbalanced data sets.…”

Section: Related Workmentioning

confidence: 99%

Improved Fuzzy-Optimally Weighted Nearest Neighbor Strategy to Classify Imbalanced Data

Patel¹,

Thakur²

2017

IJIES

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Abstract:Learning from imbalanced data is one of the burning issues of the era. Traditional classification methods exhibit degradation in their performances while dealing with imbalanced data sets due to skewed distribution of data into classes. Among various suggested solutions, instance based weighted approaches secured the space in such cases. In this paper, we are proposing a new fuzzy weighted nearest neighbor method that optimally handle the imbalance issue of data. Use of optimal weights improve the performance of fuzzy nearest neighbor algorithm for default balanced distribution of data, for the classification of imbalanced data concept of adaptive K is merged with it that apply large K, number of nearest neighbors for large class and small K for small class. We deploy this combination to classify imbalanced data with better accuracy for different evaluation measures. Experimental results affirm that our proposed method perform well than the traditional fuzzy nearest neighbor classification for these type of data sets.

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IFROWANN: Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification

Cited by 92 publications

References 47 publications

An insight into imbalanced Big Data classification: outcomes and challenges

An insight into imbalanced Big Data classification: outcomes and challenges

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

Improved Fuzzy-Optimally Weighted Nearest Neighbor Strategy to Classify Imbalanced Data

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