Open AccessJournal Article
Using AUC and Accuracy in Evaluating Learning Algorithms - Appendices.
Jin Huang,Charles X. Ling +1 more
Reads0
Chats0
About:
This article is published in IEEE Transactions on Knowledge and Data Engineering.The article was published on 2005-01-01 and is currently open access. It has received 1063 citations till now.read more
Citations
More filters
Journal ArticleDOI
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
Davide Chicco,Giuseppe Jurman +1 more
TL;DR: This article shows how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario.
Journal ArticleDOI
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
TL;DR: A taxonomy for ensemble-based methods to address the class imbalance where each proposal can be categorized depending on the inner ensemble methodology in which it is based is proposed and a thorough empirical comparison is developed by the consideration of the most significant published approaches to show whether any of them makes a difference.
Journal ArticleDOI
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
TL;DR: This work carries out a thorough discussion on the main issues related to using data intrinsic characteristics in this classification problem, and introduces several approaches and recommendations to address these problems in conjunction with imbalanced data.
Journal ArticleDOI
An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes
TL;DR: This work develops a double study, using different base classifiers in order to observe the suitability and potential of each combination within each classifier, and compares the performance of these ensemble techniques with the classifiers' themselves.
Journal ArticleDOI
An experimental comparison of performance measures for classification
TL;DR: This work analyzes experimentally the behaviour of 18 different performance metrics in several scenarios, identifying clusters and relationships between measures and makes a comprehensive analysis of the relationships between metrics, and a taxonomy and arrangement of them according to the previous traits.
References
More filters
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Book
C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Journal ArticleDOI
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
TL;DR: A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented and it is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a random chosen non-diseased subject.
Journal ArticleDOI
Pattern Classification and Scene Analysis.
Book
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.