Cost sensitive learning
Cost-Sensitive Learning for Recurrence Prediction of Breast Cancer
... a cost-sensitive learning approach to involve the censored data in prognostic assessment with better recurrence prediction ...the cost-proportionate rejection sampling and a committee machine ...
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Cost-Sensitive Learning by Cost-Proportionate Example Weighting
... of cost-sensitive learning in terms of one number per example is more general than “cost matrix” formulations which are more typical in cost-sensitive learn- ing [6, 2], when the ...
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Online Cost-Sensitive Learning for Efficient Interactive Classification
... online cost-sensitive learning approach designed for applications where a batch classifier has been trained for a given classifica- tion task and focus on optimizing the interaction be- tween the ...
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A COST SENSITIVE LEARNING METHOD TO TUNE THE NEAREST NEIGHBOUR FOR INTRUSION DETECTION
... novel cost-sensitive learning algorithm is proposed to improve the performance of the nearest neighbor for intrusion ...the learning algorithm is to minimize the total cost in ...
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Cost-Sensitive Learning with Noisy Labels
... constitutes cost-sensitive learning, and is motivated by applications and domains where misclassifica- tion cost could depend on the category of the ...consistent learning algorithms ...
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Improving specific class mapping from remotely sensed data by cost sensitive learning
... using cost-sensitive ...class. Cost-sensitive approaches are typically implemented by weighting training data points accordingly to their impor- tance to the ...the ...
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Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds
... of cost-sensitive learning techniques, the relationship between cost-sensitive learning and the specifi- cation of the model set in a parametric estimation framework remains ...
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Cost-Sensitive Learning to Rank
... overwhelmingly Cost-Sensitive or Cost-Reweighted meth- ods. The best Cost-Sensitive method is better than the best baseline, which is statistically significant in Customers Out- ages, ...
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Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data
... Dyslexia can be defined as a learning disability in people with normal intellectual coefficient, and without further physical or psychological problems that can explain such disability. The dataset “Diagnosis of ...
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A general framework for cost-sensitive boosting
... such cost-sensitive or asymmetric conditions (disaster prediction, fraud detection, medical diagnosis, object detection, ...to cost-sensitive learning [ 9 , 51 , 66 ], including a ...
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Development of new cost sensitive Bayesian network learning algorithms
... induce cost-sensitive classifiers has become a significant ...Thus, cost-sensitive learning algorithms have received increasing attention in most real world ...on ...
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Cost-Sensitive Support Vector Machine for Semi-Supervised Learning
... In many real-world applications, different misclassifications often have different costs, such as disease di- agnosis, fraud detection, business decision making [21, 22] and object recognition [23, 24] and so on. These ...
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A cost-sensitive decision tree learning algorithm based on a multi-armed bandit framework
... 5 Cost-sensitive learning could be thought of as involving two decision-makers because there is an algorithm and costs which sometimes work together well and sometimes do ...for ...
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Active Learning for Cost-Sensitive Classification
... active learning algorithm for cost-sensitive multiclass classification: problems where different errors have different ...label’s cost and predicting the ...passive learning and several ...
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Cost sensitive online multiple kernel classification
... design Cost- Sensitive Online Multiple Kernel Classification (CSOMKC) algorithms, which provide a novel method to address all the above challenges of cost-sensitive online classification (and ...
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Learning cost-sensitive Bayesian networks via direct and indirect methods
... Cost-sensitive learning algorithms have received in- creasing attention in most real world applications, though most of the existing studies are devoted to mak- ing decision trees ...develop ...
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Robust Cost Sensitive Support Vector Machine
... In consideration of the above result, we showed that robust classification models could be applied to cost sensitive learning. The presented model has been in- vestigated for some benchmark ...
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Cost sensitive decision tree learning using a multi armed bandit framework
... most cost-sensitive algorithms label leaf nodes by selecting the class that minimizes cost of misclassification, whilst accuracy based algorithms typically select the majority ...test cost of ...
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Learning cost sensitive Bayesian networks via direct and indirect methods
... Abstract. Cost-sensitive learning has become an increasingly important area that recognizes that real world classification prob- lems need to take the costs of misclassification and accuracy into ...
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Efficient learning of Arrhythmia data set with Multi class cost sensitive classifiers
... a cost matrix. The cost matrix will provide the costs associated with the four outcomes shown in the confusion matrix, which we refer to as CTP, CFP, CFN, and ...in cost- sensitive ...
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