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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 …
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 …
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 …
systems. Although ROC charts are conceptually simple, there are some common …
A machine-learning model for automatic detection of movement compensations in stroke patients
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 …
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 …
patterns wrt a correlation measure (such as information gain). By reinterpreting correlation …
Multiobjective genetic programming for maximizing ROC performance
In binary classification problems, receiver operating characteristic (ROC) graphs are
commonly used for visualizing, organizing and selecting classifiers based on their …
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 …
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 …
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 …
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 …
frequently used in the machine learning community to analyze the performance of binary …