Learning from imbalanced data
Learning from Imbalanced Data: Application to Bank Fraud Detection
... fraud data, and more generally with imbalanced data, is a complex task for most current learning algorithms because of the under-representation of frauds over ...range from sampling ...
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Editorial: Special Issue on Learning from Imbalanced Data Sets
... 3. SUMMARY OF THIS VOLUME’S CON- TRIBUTIONS (2004) In this section, we summarize the most recent developments in the area of class imbalances by describing briefly the con- tributions to this volume along with the ...
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Recursive Ensemble Approach for Incremental Learning of Non Stationary Imbalanced Data
... ABSTRACT Learning non-stationary data stream is much difficult as many real world data mining applications involve learning from imbalanced data ...of data having ...
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Incremental learning of concept drift from imbalanced data
... 2.4 Learning Concepts Drift from Imbalanced Data Both class imbalance and learning with concept drift have been independently studied by the machine learning ...for ...
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Box drawings for learning with imbalanced data
... with imbalanced data. Data classification problems having imbalanced (also called “unbalanced”) class distributions appear in many do- mains, ranging from mechanical failure detection ...
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Learning on the Border: Active Learning in Imbalanced Data Classification
... the learning performance of classification ...suffer from this phenomenon. The standard machine learning algorithms yield better prediction performance with balanced ...active learning is ...
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Integrating Data Selection and Extreme Learning Machine for Imbalanced Data
... Extreme Learning Machine (ELM) is one of the artificial neural network method that introduced by Huang, this method has very fast learning ...balance data. Common problems in real-life is ...
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Evaluation of machine learning techniques for imbalanced data in IDS
... in data mining community and a number of solutions have been proposed at the data and algorithm levels and trying to address the imbalanced data ...of imbalanced data is not ...
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Boosted SVM with active learning strategy for imbalanced data
... directly from imbalanced ...active learning strategy to eliminate redundant instances and more properly estimate misclassification costs for each of the base SVMs in the com- ...
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Semi-Supervised Self-Learning on Imbalanced Data Sets
... the data does not result in a classifier that performs best on test sets with the actual class distribution ...explore data imbalance ...missing data in the training set. Missing data ...
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Learning to improve medical decision making from imbalanced data without a priori cost
... medical data analysis, it often happens that data are composed of a minority (positive or abnormal) group and a majority (negative or normal) group and the cost of misclassifying a minority sample as a ...
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A Selective Sampling Method for Imbalanced Data Learning on Support Vector Machines
... dominated by majority SVs. That means that the decision function is more likely to classify a boundary as majority. The second reason, the weakness of the soft-margin SVMs, is an inherent weakness in coping with ...
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A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM
... Various methodologies have been developed in tackling this problem including sampling, cost-sensitive, and other hybrid ones. However, the samples near the decision boundary which contain more discriminative information ...
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Ensemble Learning With Imbalanced Data
... While precision, recall, and the F-measure are computed on unordered sets of predicted regions, L-quality is computed on ordered or ranked sets of predicted regions. Another ranking quality method is the precision-recall ...
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BUILDING CLASSIFICATION MODELS FROM IMBALANCED FRAUD DETECTION DATA
... in learning from imbalanced data sets is evaluating the learning results ...machine learning algorithms are evaluated using the standard performance metrics such as overall ...
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GPU-Accelerated Extreme Learning Machines for Imbalanced Data Streams with Concept Drift
... or data chunks. In accordance with incremental learning principles each example will be processed only once and after being processed by oELM it can be ...
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Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition
... system from raw sensor data with noise in a smart environment ...Moreover, imbalanced human activity datasets with less frequent activities create extra challenges for accurate activity ...Deep ...
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Supplementary Material: Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data
... Our architecture is based on the one proposed in StackGANv2 [4], where we use its 1-stage version for generating 64x64x3 resolution images. There is an initial fully connected layer which maps the input (concatenation of ...
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Exploiting Entity BIO Tag Embeddings and Multi task Learning for Relation Extraction with Imbalanced Data
... Abstract In practical scenario, relation extraction needs to first identify entity pairs that have relation and then assign a correct relation class. How- ever, the number of non-relation entity pairs in context ...
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Learning from Imbalanced Multi-label Data Sets by Using Ensemble Strategies
... classification learning are designed for balanced data and don’t work well on imbalanced ...produced from combination of k-nearest neighbor and logistic regression ...ensemble learning ...
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