BibSLEIGH
BibSLEIGH corpus
BibSLEIGH tags
BibSLEIGH bundles
BibSLEIGH people
CC-BY
Open Knowledge
XHTML 1.0 W3C Rec
CSS 2.1 W3C CanRec
email twitter
Used together with:
curv (10)
use (7)
under (7)
area (7)
learn (6)

Stem roc$ (all stems)

23 papers:

ICPRICPR-2010-KhreichGMS #classification
Boolean Combination of Classifiers in the ROC Space (WK, EG, AM, RS), pp. 4299–4303.
ICPRICPR-2010-PaclikLLD #analysis #classification #optimisation
ROC Analysis and Cost-Sensitive Optimization for Hierarchical Classifiers (PP, CL, TL, RPWD), pp. 2977–2980.
SEKESEKE-2010-KhoshgoftaarG #machine learning #metric #novel #re-engineering #using
Software Engineering with Computational Intelligence and Machine Learning A Novel Software Metric Selection Technique Using the Area Under ROC Curves (TMK, KG), pp. 203–208.
ECIRECIR-2009-DonmezC #learning #optimisation #rank
Active Sampling for Rank Learning via Optimizing the Area under the ROC Curve (PD, JGC), pp. 78–89.
KDDKDD-2009-NijssenGR #approach #constraints #correlation #mining #programming
Correlated itemset mining in ROC space: a constraint programming approach (SN, TG, LDR), pp. 647–656.
ICPRICPR-2008-PaclikLND #analysis #estimation #multi #using
Variance estimation for two-class and multi-class ROC analysis using operating point averaging (PP, CL, JN, RPWD), pp. 1–4.
KDDKDD-2008-ChenW #classification #feature model #metric #named #performance #problem
FAST: a roc-based feature selection metric for small samples and imbalanced data classification problems (XwC, MW), pp. 124–132.
MLDMMLDM-2007-MarroccoMT #comparison #empirical
An Empirical Comparison of Ideal and Empirical ROC-Based Reject Rules (CM, MM, FT), pp. 47–60.
ICMLICML-2006-DavisG
The relationship between Precision-Recall and ROC curves (JD, MG), pp. 233–240.
ICPRICPR-v2-2006-BarakatB #using
Rule Extraction from Support Vector Machines: Measuring the Explanation Capability Using the Area under the ROC Curve (NHB, APB), pp. 812–815.
ICPRICPR-v2-2006-GaoLL #approach #classification #learning #optimisation
An ensemble classifier learning approach to ROC optimization (SG, CHL, JHL), pp. 679–682.
ICPRICPR-v4-2006-LandgrebePD
Precision-recall operating characteristic (P-ROC) curves in imprecise environments (TL, PP, RPWD), pp. 123–127.
ICPRICPR-v4-2006-TaxD #linear #optimisation
Linear model combining by optimizing the Area under the ROC curve (DMJT, RPWD), pp. 119–122.
ICMLICML-2005-MacskassyPR #empirical #evaluation
ROC confidence bands: an empirical evaluation (SAM, FJP, SR), pp. 537–544.
ICMLICML-2005-Pietraszek #analysis #classification #optimisation #using
Optimizing abstaining classifiers using ROC analysis (TP), pp. 665–672.
ICMLICML-2004-HerschtalR #optimisation #using
Optimising area under the ROC curve using gradient descent (AH, BR).
ICMLICML-2003-Flach #comprehension #geometry #machine learning #metric
The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics (PAF), pp. 194–201.
ICMLICML-2003-LachicheF #classification #multi #probability #using
Improving Accuracy and Cost of Two-class and Multi-class Probabilistic Classifiers Using ROC Curves (NL, PAF), pp. 416–423.
MLDMMLDM-2003-Tortorella
A ROC-Based Reject Rule for Support Vector Machines (FT), pp. 106–120.
ICMLICML-2002-FerriFH #learning #using
Learning Decision Trees Using the Area Under the ROC Curve (CF, PAF, JHO), pp. 139–146.
ICPRICPR-v2-2002-Maloof #analysis #machine learning #on the #statistics #testing
On Machine Learning, ROC Analysis, and Statistical Tests of Significance (MAM), pp. 204–207.
ICPRICPR-v3-2002-JohnsonB #identification #metric
Relationship between Identification Metrics: Expected Confusion and Area Under a ROC Curve (AYJ, AFB), pp. 662–666.
KDDKDD-2000-DrummondH #representation
Explicitly representing expected cost: an alternative to ROC representation (CD, RCH), pp. 198–207.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
Hosted as a part of SLEBOK on GitHub.