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The use of the area under the ROC curve in the evaluation of machine learning algorithms

by Andrew P. Bradley - PATTERN RECOGNITION , 1997
"... In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k-Ne ..."
Abstract - Cited by 685 (3 self) - Add to MetaCart
In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k

Training Support Vector Machines: an Application to Face Detection

by Edgar Osuna, Robert Freund, Federico Girosi , 1997
"... We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision sur ..."
Abstract - Cited by 727 (1 self) - Add to MetaCart
We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision

A framework for learning predictive structures from multiple tasks and unlabeled data

by Rie Kubota Ando, Tong Zhang - JOURNAL OF MACHINE LEARNING RESEARCH , 2005
"... One of the most important issues in machine learning is whether one can improve the performance of a supervised learning algorithm by including unlabeled data. Methods that use both labeled and unlabeled data are generally referred to as semi-supervised learning. Although a number of such methods ar ..."
Abstract - Cited by 443 (3 self) - Add to MetaCart
One of the most important issues in machine learning is whether one can improve the performance of a supervised learning algorithm by including unlabeled data. Methods that use both labeled and unlabeled data are generally referred to as semi-supervised learning. Although a number of such methods

The Case Against Accuracy Estimation for Comparing Induction Algorithms

by Foster Provost, Tom Fawcett, Ron Kohavi - In Proceedings of the Fifteenth International Conference on Machine Learning , 1997
"... We analyze critically the use of classification accuracy to compare classifiers on natural data sets, providing a thorough investigation using ROC analysis, standard machine learning algorithms, and standard benchmark data sets. The results raise serious concerns about the use of accuracy for compar ..."
Abstract - Cited by 414 (23 self) - Add to MetaCart
We analyze critically the use of classification accuracy to compare classifiers on natural data sets, providing a thorough investigation using ROC analysis, standard machine learning algorithms, and standard benchmark data sets. The results raise serious concerns about the use of accuracy

Applying support vector machines to imbalanced datasets

by Rehan Akbani, Stephen Kwek, Nathalie Japkowicz - In Proceedings of the 15th European Conference on Machine Learning (ECML , 2004
"... Abstract. Support Vector Machines (SVM) have been extensively studied and have shown remarkable success in many applications. However the success of SVM is very limited when it is applied to the problem of learning from imbalanced datasets in which negative instances heavily outnumber the positive i ..."
Abstract - Cited by 154 (2 self) - Add to MetaCart
Abstract. Support Vector Machines (SVM) have been extensively studied and have shown remarkable success in many applications. However the success of SVM is very limited when it is applied to the problem of learning from imbalanced datasets in which negative instances heavily outnumber the positive

Learning on the border: Active learning in imbalanced data classification

by S Eyda Ertekin, Jian Huang, Léon Bottou, C. Lee Giles - In Proc. ACM Conf. on Information and Knowledge Management (CIKM ’07 , 2007
"... This paper is concerned with the class imbalance problem which has been known to hinder the learning performance of classification algorithms. The problem occurs when there are significantly less number of observations of the target concept. Various real-world classification tasks, such as medical d ..."
Abstract - Cited by 51 (3 self) - Add to MetaCart
diagnosis, text categorization and fraud detection suffer from this phenomenon. The standard machine learning algorithms yield better prediction performance with balanced datasets. In this paper, we demonstrate that active learning is capable of solving the class imbalance problem by providing the learner

Mining Imbalanced Data with Learning Classifier Systems

by Albert Orriols-puig
"... Summary. This chapter investigates the capabilities of XCS for mining imbalanced datasets. Initial experiments show that, for moderate and high class imbalances, XCS tends to evolve a large proportion of overgeneral classifiers. Theoretical analyses are developed, deriving an imbalance bound up to w ..."
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Summary. This chapter investigates the capabilities of XCS for mining imbalanced datasets. Initial experiments show that, for moderate and high class imbalances, XCS tends to evolve a large proportion of overgeneral classifiers. Theoretical analyses are developed, deriving an imbalance bound up

Cost-sensitive boosting for classification of imbalanced data

by Yanmin Sun , Mohamed S. Kamel , Andrew K. C. Wong , Yang Wang , 2007
"... Classification of data with imbalanced class distribution has posed a significant drawback of the performance attainable by most standard classifier learning algorithms, which assume a relatively balanced class distribution and equal misclassification costs. The significant difficulty and frequent o ..."
Abstract - Cited by 77 (1 self) - Add to MetaCart
occurrence of the class imbalance problem indicate the need for extra research efforts. The objective of this paper is to investigate meta-techniques applicable to most classifier learning algorithms, with the aim to advance the classification of imbalanced data. The AdaBoost algorithm is reported as a

Boosting Support Vector Machines for Imbalanced Data Sets

by Benjamin X. Wang, Nathalie Japkowicz
"... Real world data mining applications must address the issue of learning from imbalanced data sets. The problem occurs when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed vect ..."
Abstract - Cited by 28 (0 self) - Add to MetaCart
Real world data mining applications must address the issue of learning from imbalanced data sets. The problem occurs when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed

Dealing with severely imbalanced data

by William Klement, Szymon Wilk, Wojtek Michaowski, Stan Matwin - Workshop on Data Mining When Classes are Imbalanced and Errors Have Costs, PAKDD , 2009
"... Abstract. In many practical domains, machine learning algorithms face a difficult challenge when the data is severely imbalanced. The situation gets even worse when the size of the minority class is very small. Recently, machine learning researchers have called for the use of several techniques to a ..."
Abstract - Cited by 3 (1 self) - Add to MetaCart
Abstract. In many practical domains, machine learning algorithms face a difficult challenge when the data is severely imbalanced. The situation gets even worse when the size of the minority class is very small. Recently, machine learning researchers have called for the use of several techniques
Results 1 - 10 of 893