[PDF] Top 20 Efficient learning of Arrhythmia data set with Multi class cost sensitive classifiers
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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 ... See full document
5
Active Learning for Cost-Sensitive Classification
... best learning rates for different datasets and mellowness settings are in Table ...test cost along with bars extending from the 15 th to 85 th ...active learning achieves similar test cost as ... See full document
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Class imbalance ensemble learning based on the margin theory
... each class in a data-set plays an important role in machine ...world data often suffer from class imbalance. Dealing with multi-class tasks with different ... See full document
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Cost-Sensitive Learning with Noisy Labels
... at learning in the presence of label noise was by Bylander (1994) that concerned learnability of linear threshold functions (LTFs) in the Probably Approximately Correct (PAC) ...online learning algorithms ... See full document
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An Efficient Elephant Flow Detection with Cost-Sensitive in SDN
... and data plane, which provides better network management and higher utilization for data center ...low cost still remains a challenge task in current ...introduce cost-sensitive ... See full document
5
Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds
... the cost-sensitive learning problem becomes one of density estimation, and the costs affect only the threshold, not the ...model set leading to complex ...real-world data sets, ... See full document
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An overview of classification algorithms for imbalanced datasets
... imbalanced data sets. At data level, sampling is the most common approach to deal with imbalanced ...the class imbalance, adjusting the probabilistic estimate at the treeleaf (when working with ... See full document
6
Cost sensitive decision tree learning using a multi armed bandit framework
... majority class as the label (Weiss and Kulikowski 1991) or measuring a split in some way and if this falls below a specified threshold stopping the process (Han and Kamber ...training set can be ... See full document
201
Second Order Online Active Learning based Malicious Web Classification
... active learning gives a larger number of advantages than in the stationary simple of the ...least cost, dynamic learning is a ceaselessly developing region in ML ...working set assurance in ... See full document
5
A cost sensitive decision tree learning algorithm based on a multi armed bandit framework
... test cost which is within this range and remains consistent throughout the experiments when that attribute is ...The cost to classify is calculated and normalized as per Turney’s method described in ...to ... See full document
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Improving specific class mapping from remotely sensed data by cost sensitive learning
... of multi-class classification problems have attracted significant attention in machine learning research and has been shown to perform well in most multi-class problem (Krawczyk et ... See full document
21
GPCRTree: online hierarchical classification of GPCR function
... GPCR Class, Sub- family and Sub-Subfamily ...GPCR class tree becomes a flat classification problem solved using a standard classifier [25,26], obviating the need to devise a novel ...the data are ... See full document
5
A Comparison of Models for Cost Sensitive Active Learning
... Active Learning (AL) is a selective sam- pling strategy which has been shown to be particularly cost-efficient by drastically reducing the amount of training data to be manually ...language ... See full document
9
Online Active Learning for Cost Sensitive Domain Adaptation
... online learning on domain adap- tation and proposed to combine multiple similar source domains to perform online learning for the target domain, which provides a new opportunity for conducting active ... See full document
9
Evolving single and multi model fuzzy classifiers with FLEXFIS class
... Two variants for evolving fuzzy classification schemes were presented, FLEXFIS-Class SM based on single-model architecture and FLEXFIS-Class MM based on multi-model architecture. The key issues of ... See full document
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An Efficient Algorithm to Analyse the Cost Sensitive Measures on Datasets
... Cost sensitive classification is an important problem in data mining which has to address varied misclassification cost ...misclassification cost can be quite ...positive class ... See full document
6
RULES REDUCTION AND OPTIMIZATION OF FUZZY LOGIC MEMBERSHIP FUNCTIONS FOR INDUCTION MOTOR SPEED CONTROLLER
... This learning is well adapted for single concept, but the complexity arises when the object have ...supervised learning to adapt the multi-labels data objects are found in ...dependence ... See full document
9
A fast-response production-inventory model for deteriorating seasonal products with learning in set-ups Pages 715-736 Download PDF
... Panda et al. (2008) developed an inventory model for deteriorating seasonal products using ramp-type demand pattern with a three-phase variation in demand. The demand pattern in this case is assumed to increase ... See full document
22
Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers
... different classifiers using the one-sided paired t-test for the 6 TIMIT-4/6 data sets: Each entry of the table gives the significance of the difference of the classifi- cation rate of two classifiers ... See full document
38
Considering Cost Asymmetry in Learning Classifiers
... All learning al- gorithms considered in this paper involve using a convex surrogate to the correct non differentiable non convex loss ...non-optimal classifiers. We thus propose to generate all possible ... See full document
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