Dynamic affinity-based classification of multi-class imbalanced data with one-versus-one decomposition: a fuzzy rough set approach

Vluymans, Sarah; Fernández, Alberto; Saeys, Yvan; Cornelis, Chris; Herrera, Francisco

doi:10.1007/s10115-017-1126-1

Cited by 30 publications

(28 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Fuzzy Rough OVO COmbination (FROVOCO) [18] is an ensemble classifier specifically designed for, but not restricted to, imbalanced data, which adapts itself to the Imbalance Ratio (IR) between classes. It balances one-versus-one decomposition with two global class afinity measures (Table 4).…”

Section: Fuzzy Rough Ovo Combination (Frovoco) Multiclass Classificationmentioning

confidence: 99%

“…In this paper, we present an initial version of fuzzy-roughlearn, a Python library that fills this gap. At present, it includes FRNN, FRFS, FRPS, as well as FROVOCO [18] and FRONEC [16], two more recent algorithms designed for imbalanced and multilabel classification. These implementations all make use of a significant modification of classical fuzzy rough set theory: the incorporation of Ordered Weighted Averaging (OWA) operators in the calculation of upper and lower approximations for increased robustness [1].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

fuzzy-rough-learn 0.1: A Python Library for Machine Learning with Fuzzy Rough Sets

2020

Self Cite

View full text Add to dashboard Cite

We present fuzzy-rough-learn, the first Python library of fuzzy rough set machine learning algorithms. It contains three algorithms previously implemented in R and Java, as well as two new algorithms from the recent literature. We briefly discuss the use cases of fuzzy-roughlearn and the design philosophy guiding its development, before providing an overview of the included algorithms and their parameters.

show abstract

Section: Fuzzy Rough Ovo Combination (Frovoco) Multiclass Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

fuzzy-rough-learn 0.1: A Python Library for Machine Learning with Fuzzy Rough Sets

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…For imbalanced multiple classes classification problems, (Fernandez et al, 2013) have done an ex-perimental analysis to determine the behaviour of the different approaches proposed in the specialized literature. (Vluymans et al, 2017) also proposed an extension to multi-class data using one-vs-one decomposition.…”

Section: Resample Techniques For Imbalanced Datamentioning

confidence: 99%

A Practical Guide to Support Change-proneness Prediction

Melo

Cruz

Martins

et al. 2019

Proceedings of the 21st International Conference on Enterprise Information Systems

View full text Add to dashboard Cite

During the development and maintenance of a system of software, changes can occur due to new features, bug fix, code refactoring or technological advancements. In this context, software change prediction can be very useful in guiding the maintenance team to identify change-prone classes in early phases of software development to improve their quality and make them more flexible for future changes. A myriad of related works use machine learning techniques to lead with this problem based on different kinds of metrics. However, inadequate description of data source or modeling process makes research results reported in many works hard to interpret or reproduce. In this paper, we firstly propose a practical guideline to support change-proneness prediction for optimal use of predictive models. Then, we apply the proposed guideline over a case study using a large imbalanced data set extracted from a wide commercial software. Moreover, we analyze some papers which deal with change-proneness prediction and discuss them about missing points.

show abstract

“…Fuzzy Rough Nearest Neighbour (FRNN) classification [4] D. Peralta is with the Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent University, e-mail: daniel.peralta@irc.vib-ugent.be robust through the addition of Ordered Weighted Averaging (OWA) operators [5], [6]. The resulting combination (FRNN-OWA) has been shown to be particularly effective for various types of imbalanced classification [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

Scalable Approximate FRNN-OWA Classification

Lenz

Peralta

Cornelis

2020

IEEE Trans. Fuzzy Syst.

Self Cite

View full text Add to dashboard Cite

Fuzzy Rough Nearest Neighbour classification with Ordered Weighted Averaging operators (FRNN-OWA) is an algorithm that classifies unseen instances according to their membership in the fuzzy upper and lower approximations of the decision classes. Previous research has shown that the use of OWA operators increases the robustness of this model. However, calculating membership in an approximation requires a nearest neighbour search. In practice, the query time complexity of exact nearest neighbour search algorithms in more than a handful of dimensions is near-linear, which limits the scalability of FRNN-OWA. Therefore, we propose approximate FRNN-OWA, a modified model that calculates upper and lower approximations of decision classes using the approximate nearest neighbours returned by Hierarchical Navigable Small Worlds (HNSW), a recent approximative nearest neighbour search algorithm with logarithmic query time complexity at constant near-100% accuracy. We demonstrate that approximate FRNN-OWA is sufficiently robust to match the classification accuracy of exact FRNN-OWA while scaling much more efficiently. We test four parameter configurations of HNSW, and evaluate their performance by measuring classification accuracy and construction and query times for samples of various sizes from three large datasets. We find that with two of the parameter configurations, approximate FRNN-OWA achieves near-identical accuracy to exact FRNN-OWA for most sample sizes within query times that are up to several orders of magnitude faster.

show abstract

Dynamic affinity-based classification of multi-class imbalanced data with one-versus-one decomposition: a fuzzy rough set approach

Cited by 30 publications

References 66 publications

fuzzy-rough-learn 0.1: A Python Library for Machine Learning with Fuzzy Rough Sets

fuzzy-rough-learn 0.1: A Python Library for Machine Learning with Fuzzy Rough Sets

A Practical Guide to Support Change-proneness Prediction

Scalable Approximate FRNN-OWA Classification

Contact Info

Product

Resources

About