Results 241 to 250 of about 106,507 (280)

A Robust Classifier for Imbalanced Datasets

2014
Imbalanced dataset classification is a challenging problem, since many classifiers are sensitive to class distribution so that the classifiers’ prediction has bias towards majority class. Hellinger Distance has been proven that it is skew-insensitive and the decision trees that employ Hellinger Distance as a splitting criterion have shown better ...
Sori Kang, Kotagiri Ramamohanarao
openaire   +1 more source

An Improved Measurement of the Imbalanced Dataset

2018
Imbalanced classification is a classification problem that violates the assumption of uniform distribution of samples. In such problems, traditional imbalanced datasets are measured in terms of the imbalance of sample size, without considering the distribution information, which has a more important impact on the classification performance, so the ...
Chunkai Zhang, Ying Zhou, Yingyang Chen, Changqing Qi, Xuan Wang 0002, Lifeng Dong   +5 more
openaire   +1 more source

Discrimination aware classification for imbalanced datasets

Proceedings of the 22nd ACM international conference on Information & Knowledge Management, 2013
The problem of learning a discrimination aware model has recently received attention in the data mining community. Various methods and improved models have been proposed, with the main approach being the detection of a discrimination sensitive attribute.
Goce Ristanoski, Wei Liu 0007, James Bailey 0001   +2 more
openaire   +1 more source

Comparing SVM ensembles for imbalanced datasets

2010 10th International Conference on Intelligent Systems Design and Applications, 2010
Real life datasets often suffer from the problem of class imbalance, which thwarts supervised learning process. In such data sets examples of positive (minority) class are significantly less than those of negative (majority) class leading to severe class imbalance.
Vasudha Bhatnagar, Manju Bhardwaj, Ashish Mahabal   +2 more
openaire   +1 more source

Boosting prediction performance on imbalanced dataset

International Journal of Information and Communication Technology, 2018
Mining from imbalance data is an important problem in algorithmic and performance evaluation. When a dataset is imbalanced, the classification technique is not equal considering both the classes. It is obvious that the standard classifiers are not suitable to deal with imbalanced data, since they will likely classify all the instances into the majority
Masoumeh Zareapoor, Pourya Shamsolmoali
openaire   +1 more source

Simulating Complexity Measures on Imbalanced Datasets

2020
Classification tasks using imbalanced datasets are not challenging on their own. Classification models perform poorly on the minority class when the datasets present other difficulties, such as class overlap and complex decision border. Data complexity measures can identify such difficulties, better dealing with imbalanced datasets.
Victor H. Barella, Luís Paulo F. Garcia, André C. P. L. F. de Carvalho   +2 more
openaire   +1 more source

New construction of Ensemble Classifiers for imbalanced datasets

2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, 2010
Learning in the presence of data imbalances presents a great challenge to machine learning. Imbalanced data sets represent a significant problem because the corresponding classifier has a tendency to ignore samples which have smaller representation in the training sets.
Yun Zhai, Da Ruan 0001, Nan Ma, Bing An
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A comparison for handling imbalanced datasets

2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA), 2014
In various real case, imbalanced datasets problems are inevitable, such as in metal detecting security or diagnosis of disease. With the limitations of existing learning algorithms when faced with imbalanced datasets, the prediction error is caused by the dominance of the majority against the minority class. Various techniques have been made to address
Arif Syaripudin, Masayu Leylia Khodra
openaire   +1 more source

An Adaptive Oversampling Technique for Imbalanced Datasets

2018
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 ...
Shaukat Ali Shahee, Usha Ananthakumar
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A Practical Anonymization Approach for Imbalanced Datasets

IT Professional, 2022
Abdul Majeed 0001, Seong Oun Hwang
openaire   +1 more source