Research in machine learning, statistics and related elds has produced a wide variety of algorithms for classiication. However, most of these algorithms assume that all errors have the same cost, which is seldom the case in KDD problems. Individually making each classiication learner cost-sensitive is laborious, and often non-trivial. In this paper we propose a principled method for making an a...

This paper presents a new decision tree learning algorithm that takes account of costs of misclassification. The algorithm is based on the hypothesis that non-linear decision nodes provide a better basis for cost-sensitive induction than axis-parallel decision nodes and utilizes discriminant analysis to construct non-linear cost-sensitive decision trees. The performance of the algorithm is eval...

This paper addresses two cost-sensitive learning methodology issues. First, we ask the question of whether Bagging is always an appropriate procedure to compute accurate class-probability estimates for cost-sensitive classiication. Second, we will point the reader to a potential source of erroneous results in the most common procedure of evaluating cost-sensitive classiiers when the real miscla...

Loan fraud is a critical factor in the insolvency of financial institutions, so companies make an effort to reduce the loss from fraud by building a model for proactive fraud prediction. However, there are still two critical problems to be resolved for the fraud detection: (1) the lack of cost sensitivity between type I error and type II error in most prediction models, and (2) highly skewed di...

BACKGROUND Myocardial infarction (MI) occurs due to heart muscle death that costs like human life, which is higher than the treatment costs. This study aimed to present an MI prediction model using classification data mining methods, which consider the imbalance nature of the problem. METHODS We enrolled 455 healthy and 295 myocardial infarction cases of visitors to Shahid Madani Specialized ...

One of the most active areas of research in supervised learning has been the study of methods for constructing good ensembles of classiiers, that is, a set of classi-ers whose individual decisions are combined to increase overall accuracy of classifying new examples. In many applications classiiers are required to minimize an asym-metric loss function rather than the raw misclassiication rate. ...

In this thesis we study the classification task in the presence of class imbalanced data. This task arises in many applications when we are interested in the under-represented (minority) classes. Examples of such applications are related to fraud detection, medical diagnosis and monitoring, text categorization, risk management, information retrieval and filtering. Although there exist many stan...

The number of normal samples wind turbine generators is much larger than the fault samples. To solve problem imbalanced classification in generator detection, a cost-sensitive extremely randomized trees (CS-ERT) algorithm proposed this paper, which learning method introduced into an (ERT) algorithm. Based on misclassification cost and class distribution, gain (MCG) as score measure CS-ERT model...

background: myocardial infarction (mi) occurs due to heart muscle death that costs like human life, which is higher than the treatment costs. this study aimed to present an mi prediction model using classification data mining methods, which consider the imbalance nature of the problem. methods: we enrolled 455 healthy and 295 myocardial infarction cases of visitors to shahid madani specialized ...