Shaukat Shahee

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Steven Pinker

Harvard University

Bert Vaux

University of Cambridge

Julien Eynard

Université of Perpignan Via Domitia

David Seamon

Kansas State University

Armando Marques-Guedes

UNL - New University of Lisbon

Fabio Cuzzolin

Oxford Brookes University

Vania V Estrela

UFF - Universidade Federal Fluminense

Bogdan Gabrys

University of Technology Sydney

Tribeni Prasad Banerjee

West Bengal University Of Technology

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Papers by Shaukat Shahee

An overlap sensitive neural network for class imbalanced data

Data Mining and Knowledge Discovery

An Adaptive Oversampling Technique for Imbalanced Datasets

Advances in Data Mining. Applications and Theoretical Aspects, 2018

Class imbalance is one of the challenging problems in classification domain of data mining. This ... more Class imbalance is one of the challenging problems in classification domain of data mining. This is particularly so because of the inability of the classifiers in classifying minority examples correctly when data is imbalanced. Further, the performance of the classifiers gets deteriorated due to the presence of imbalance within class in addition to between class imbalance. Though class imbalance has been well addressed in literature, not enough attention has been given to within class imbalance. In this paper, we propose a method that can adaptively handle both between-class and within-class imbalance simultaneously and also that can take into account the spread of the data in the feature space. We validate our approach using 12 publicly available datasets and compare the classification performance with other existing oversampling techniques. The experimental results demonstrate that the proposed method is statistically superior to other methods in terms of various accuracy measures.

An effective distance based feature selection approach for imbalanced data

Applied Intelligence, 2019

Class imbalance is one of the critical areas in classification. The challenges become more severe... more Class imbalance is one of the critical areas in classification. The challenges become more severe when the data set has a large number of features. Traditional classifiers generally favour the majority class because of skewed class distributions. In recent years, feature selection is being used to select the appropriate features for better classification of minority class. However, these studies are limited to imbalance that arise between the classes. In addition to between class imbalance, within class imbalance, along with large number of features, adds additional complexity and results in poor performance of the classifier. In the current study, we propose an effective distance based feature selection method (ED-Relief) that uses a sophisticated distance measure, in order to tackle simultaneous occurrence of between and within class imbalance. This method has been tested on a variety of simulated experiments and real life data sets and the results are compared with the traditional Relief method and some of the well known recent distance based feature selection methods. The results clearly show the superiority of the proposed effective distance based feature selection method.

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Synthetic sampling approach based on model-based clustering for imbalanced data

International Journal of Artificial Intelligence and Soft Computing, 2017

A dataset exhibits class imbalance problem when one class has very few examples compared to the o... more A dataset exhibits class imbalance problem when one class has very few examples compared to the other class also referred to as between class imbalance. Apart from between-class imbalance, imbalance within classes where classes are composed of different number of sub-clusters with these sub-clusters containing different number of examples may also affect the performance of the classifier. In this paper, we propose a method that can handle both between-class and within-class imbalance simultaneously that also takes into consideration various data intrinsic characteristics. The proposed method uses model-based clustering with respect to classes to identify the sub-clusters present in the dataset and oversamples examples in each sub-cluster in such a manner that it eliminates between class and within class imbalance simultaneously. We validate our approach using neural network on ten publicly available datasets. The experimental results show the proposed method to be statistically significantly superior to other methods.