[PDF] Top 20 Class imbalance ensemble learning based on the margin theory
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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 ...from class imbalance. Dealing with multi-class tasks with different misclassification costs of classes is harder than dealing ... See full document
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Class imbalance ensemble learning based on the margin theory
... internal imbalance sampling based ensemble approaches should work better ...before learning a model ...rare class examples. In each round, the weights for minority class examples ... See full document
29
Ensemble based novel class identification for Class Imbalance under sampled Data
... the class imbalance ...one class is much smaller than from another. This is known as class imbalance problem and is often reported as an obstacle for constructing a model that can ... See full document
5
Semi-supervised learning and fairness-aware learning under class imbalance
... within class-imbalance (group-imbalance) forces the models to misclassify minority segments disproportionately compared to other segments since they are not able to learn all population segments ... See full document
148
A Review of Class Imbalance Problem
... an ensemble based learning approach (DataBoost-IM) that combined boosting with data ...Another ensemble in a hierarchical frame was proposed by Zhang and Luo ...approach based on ... See full document
9
A Systematic Study of Online Class Imbalance Learning with Concept Drift
... and class imbalance, due to the rising need from practical problems [87] ...an ensemble of classifiers trained on a more balanced set of data through resampling and overcomes concept drift passively ... See full document
19
A balanced approach to the multi-class imbalance problem
... the learning process is either supervised and ...Unsupervised learning tasks aim to gather observations into clusters, where the ideal outcome involves the formulation of groups of observations with similar ... See full document
141
Survey on deep learning with class imbalance
... C2C: class-to-class; CE: cross entropy; CNN: convolutional neural network; CRL: class rectification loss; CSDBN-DE: cost-sensitive deep belief network with differ- ential evolution; CSDNN: ... See full document
54
Projection based ensemble learning for ordinal regression
... each class from the remaining ones taking ordinal ranks into ...the ensemble literature were used, the product and the ...multiclass imbalance problems pose important dif fi culties for machine ... See full document
13
Resampling-Based Ensemble Methods for Online Class Imbalance Learning
... in class- conditional probability density functions, we look into the performance of OOB and UOB when tackling data streams with imbalance status changes ...in class prior ...faulty class is ... See full document
14
Ensemble based Active Learning for Parse Selection
... The configurational feature set is based on the deriva- tion tree features described by Toutanova etal. (2003) and takes into account parent, grandparent, and sibling relationships among the nodes of the trees ... See full document
8
A hybrid approach to the problem of class imbalance
... A useful advantage of our proposed approach is that it is equally applicable to both balanced and unbalanced datasets. Previous work [20] has shown that aside from the consideration of balance in the distribution of ... See full document
8
Novel and Recurring Class Detection using Ensemble of Classifiers: A Class based Approach
... Several studies are present in the literature on data stream classi- fication [1, 3–5, 8, 13–17]. It has been observed that, existing ap- proaches can be divided into two categories. First one is single model approach ... See full document
9
Cluster-based semi-supervised ensemble learning
... For many SSC algorithms, if the classifier assigns the same class to every unlabelled instance, the training error will be in a local optimum [Mann and McCallum, 2007]. This fact is due to the loss function ... See full document
201
Active Learning for Word Sense Disambiguation with Methods for Addressing the Class Imbalance Problem
... The ability of the active learner can be referred to as selective sampling, of which two major schemes exist: uncertainty sampling and committee-based sampling. The former method, for example pro- posed by Lewis ... See full document
8
Models of teaching and learning identified in Whole Class Ensemble Tuition
... and learning, sometimes the words employed carry a ‘baggage’ that is not immediately apparent to a reader from a different ...and learning in music (inter alia Folkestad, 2006; Green, 2008; Price & ... See full document
12
The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluations
... as class labels, we have uniformly distributed ...one class appears in 70/256 cases (when the HW equals ...machine learning techniques often classify all measurements as the majority class ... See full document
34
An ensemble based feature selection methodology for case based learning
... following learning activities: (1) practicing real-world case(s) before and outside the class to determine the treatment of patients in an easy to use manner, (2) identifying the components of a medical ... See full document
234
Ensemble-based Supervised Learning for Predicting Diabetes Onset
... Further research into available predictive models for diabetes onset did reveal some tools that use these biomarkers [16] and there is evidence that they predict cases slightly better than their simplistic counterparts. ... See full document
129
Class Imbalance Problem in Data Mining: Review
... for learning from imbalanced ...of class imbalance in its ...the class labels for a given set of training ...one class far exceeds the number belonging to the other class, the ... See full document
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