GP for Classification of Imbalanced Data
A Classification Framework for Imbalanced Data
... raw data to occur at an explosive ...and data engineering research to play an essential role in a wide range of applications from daily life to national security, from enterprise information processing to ...
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Classification Boosting in Imbalanced Data
... The classification technique aims to find a decision function that accurately predicts the class of testing data derived from the same distribution function as the data for ...the ...
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Ensemble Approach for the Classification of Imbalanced Data
... 2 School of Medicine, Griffith University [email protected] Abstract. Ensembles are often capable of greater prediction accuracy than any of their individual members. As a consequence of the diver- sity between ...
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Predictive Data Mining for Highly Imbalanced Classification
... of data mining, focusing on predictive data mining, where two central types of prediction problems are discussed: classification and ...predictive data mining, where the time-stamped ...
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An evolutionary sampling approach for classification with imbalanced data
... practical classification problems in which the number of instances of a particular class is much lower/higher than the instances of the other classes, one commonly adopted strategy is to train the classifier over ...
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Borderline Over-sampling for Imbalanced Data Classification
... In this paper, we focus on over-sampling methods with SVMs as base classifiers to deal with the data imbalance problem. For our approach, artificial minority class instances will be generated around the borderline ...
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Evolutionary Undersampling for Imbalanced Big Data Classification
... Among data sampling strategies, evolutionary undersam- pling (EUS, [13]) aims at selecting the best subset of instances from the original training set to reduce the effect of the class ...standard ...
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Classification of Imbalanced Data with a Geometric Digraph Family
... training data set, and hence the ensemble classifier preserves the information on the entire data set given by a collection of unbiased ...the data set, cluster based sampling schemes divide each ...
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On the Classification of Imbalanced Datasets
... of data patterns is very ...Microcalcification classification is one classical example for imbalanced data ...of imbalanced data sets, and investigate the performance of ...
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Random Forest Based Imbalanced Data Cleaning and Classification
... training data is not balanced, ...this data cleaning method is quite ...training data will potentially reduce the false negative rate of the model trained from ...
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Classification of high dimensional and imbalanced hyperspectral imagery data
... the classification of hyperspectral imagery acquired by the Airborne Visible/Infrared Imaging Spectrometrer (AVIRIS 1 ...image data present both very high dimensionality and multiple imbalanced ...
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Logistic regression methods for classification of imbalanced data sets
... binary classification, data sets are said to be imbalanced, if the number of negative instances are heavily larger than the positive ones (Akbani et ...two-class classification of ...
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An insight into imbalanced Big Data classification: outcomes and challenges
... Instance weighting SVM Another approach based on SVMs can be found in [50]. In the aforementioned research, authors combined an instance- weighted variant of the SVM with a Parallel Meta-learning algorithm using ...
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Classification of Imbalanced Marketing Data with Balanced Random Sets
... With imbalanced data a classifier built using all of the data has the tendency to ignore the minority ...Proper data pre-processing is very important for the effectiveness of the whole system, ...
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Overlap-based undersampling for improving imbalanced data classification.
... In this paper, we propose a new undersampling framework that reduces the dominance of the majority class instances and more importantly removes them from the overlapping region. For convenience, we refer to our ...
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A review on classification of imbalanced data for wireless sensor networks
... balanced data, so for imbalance issue, this could be helpful to use fuzzy membership concept with some strategy to deal with ...for imbalanced environment of datasets which use an adaptive inference ...
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Learning on the Border: Active Learning in Imbalanced Data Classification
... real-world classification tasks, such as medical diagnosis, text categorization and fraud detection suffer from this ...in imbalanced data ...
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Cost Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data
... training data, that are common in literature (Ling and Sheng, ...of classification accuracy is related to the dissimilarity of that other dataset and hence is to be obtained experimentally through hyperpa- ...
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Parallel Heterogeneous Voting Ensemble for Effective Classification of Imbalanced Data
... times, data deluge is an inevitable phenomenon that emerges from modern electronic gadgets that we use in our day-to-day ...voluminous data is a complex task. In particular, handling massive ...
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Optimization of data resampling through GA for the classification of imbalanced datasets
... SU 15 85 79 13 OGAR 40 85 88 15 SBR (9,25) 17 92 88 8 From the analysis of the results reported in Table 2 and that concern the UCI datasets the advantage that derives from the exploitation of the proposed techniques ...
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