The relationship between Precision-Recall and ROC curves

J Davis, M Goadrich - Proceedings of the 23rd international conference …, 2006‏ - dl.acm.org
Receiver Operator Characteristic (ROC) curves are commonly used to present results for
binary decision problems in machine learning. However, when dealing with highly skewed …

[ספר][B] Foundations of rule learning

J Fürnkranz, D Gamberger, N Lavrač - 2012‏ - books.google.com
Rules–the clearest, most explored and best understood form of knowledge representation–
are particularly important for data mining, as they offer the best tradeoff between human and …

Evaluating classifiers using ROC curves

RC Prati, GEAPA Batista… - IEEE Latin America …, 2008‏ - ieeexplore.ieee.org
ROC charts have recently been introduced as a powerful tool for evaluation of learning
systems. Although ROC charts are conceptually simple, there are some common …

A machine-learning model for automatic detection of movement compensations in stroke patients

S Kashi, RF Polak, B Lerner, L Rokach… - … on Emerging Topics …, 2020‏ - ieeexplore.ieee.org
During the process of rehabilitation after stroke, it is important that patients know how well
they perform their exercise, so they can improve their performance in future repetitions …

Correlated itemset mining in ROC space: a constraint programming approach

S Nijssen, T Guns, L De Raedt - Proceedings of the 15th ACM SIGKDD …, 2009‏ - dl.acm.org
Correlated or discriminative pattern mining is concerned with finding the highest scoring
patterns wrt a correlation measure (such as information gain). By reinterpreting correlation …

Multiobjective genetic programming for maximizing ROC performance

P Wang, K Tang, T Weise, EPK Tsang, X Yao - Neurocomputing, 2014‏ - Elsevier
In binary classification problems, receiver operating characteristic (ROC) graphs are
commonly used for visualizing, organizing and selecting classifiers based on their …

Convex hull-based multiobjective genetic programming for maximizing receiver operating characteristic performance

P Wang, M Emmerich, R Li, K Tang… - IEEE Transactions on …, 2014‏ - ieeexplore.ieee.org
The receiver operating characteristic (ROC) is commonly used to analyze the performance
of classifiers in data mining. An important topic in ROC analysis is the ROC convex hull …

An integrated approach to learning Bayesian networks of rules

J Davis, E Burnside, I de Castro Dutra, D Page… - … Learning: ECML 2005 …, 2005‏ - Springer
Abstract Inductive Logic Programming (ILP) is a popular approach for learning rules for
classification tasks. An important question is how to combine the individual rules to obtain a …

Data intrinsic characteristics

A Fernández, S García, M Galar, RC Prati… - … from imbalanced data …, 2018‏ - Springer
Although class imbalance is often pointed out as a determinant factor for degradation in
classification performance, there are situations in which good performance can be achieve …

Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms

J Zhao, VB Fernandes, L Jiao, I Yevseyeva… - Information …, 2016‏ - Elsevier
The receiver operating characteristic (ROC) and detection error tradeoff (DET) curves are
frequently used in the machine learning community to analyze the performance of binary …