## Stem roc$ (all stems)

### 23 papers:

- ICPR-2010-KhreichGMS #classification
- Boolean Combination of Classifiers in the ROC Space (WK, EG, AM, RS), pp. 4299–4303.
- ICPR-2010-PaclikLLD #analysis #classification #optimisation
- ROC Analysis and Cost-Sensitive Optimization for Hierarchical Classifiers (PP, CL, TL, RPWD), pp. 2977–2980.
- SEKE-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.
- ECIR-2009-DonmezC #learning #optimisation #rank
- Active Sampling for Rank Learning via Optimizing the Area under the ROC Curve (PD, JGC), pp. 78–89.
- KDD-2009-NijssenGR #approach #constraints #correlation #mining #programming
- Correlated itemset mining in ROC space: a constraint programming approach (SN, TG, LDR), pp. 647–656.
- ICPR-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.
- KDD-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.
- MLDM-2007-MarroccoMT #comparison #empirical
- An Empirical Comparison of Ideal and Empirical ROC-Based Reject Rules (CM, MM, FT), pp. 47–60.
- ICML-2006-DavisG
- The relationship between Precision-Recall and ROC curves (JD, MG), pp. 233–240.
- ICPR-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.
- ICPR-v2-2006-GaoLL #approach #classification #learning #optimisation
- An ensemble classifier learning approach to ROC optimization (SG, CHL, JHL), pp. 679–682.
- ICPR-v4-2006-LandgrebePD
- Precision-recall operating characteristic (P-ROC) curves in imprecise environments (TL, PP, RPWD), pp. 123–127.
- ICPR-v4-2006-TaxD #linear #optimisation
- Linear model combining by optimizing the Area under the ROC curve (DMJT, RPWD), pp. 119–122.
- ICML-2005-MacskassyPR #empirical #evaluation
- ROC confidence bands: an empirical evaluation (SAM, FJP, SR), pp. 537–544.
- ICML-2005-Pietraszek #analysis #classification #optimisation #using
- Optimizing abstaining classifiers using ROC analysis (TP), pp. 665–672.
- ICML-2004-HerschtalR #optimisation #using
- Optimising area under the ROC curve using gradient descent (AH, BR).
- ICML-2003-Flach #comprehension #geometry #machine learning #metric
- The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics (PAF), pp. 194–201.
- ICML-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.
- MLDM-2003-Tortorella
- A ROC-Based Reject Rule for Support Vector Machines (FT), pp. 106–120.
- ICML-2002-FerriFH #learning #using
- Learning Decision Trees Using the Area Under the ROC Curve (CF, PAF, JHO), pp. 139–146.
- ICPR-v2-2002-Maloof #analysis #machine learning #on the #statistics #testing
- On Machine Learning, ROC Analysis, and Statistical Tests of Significance (MAM), pp. 204–207.
- ICPR-v3-2002-JohnsonB #identification #metric
- Relationship between Identification Metrics: Expected Confusion and Area Under a ROC Curve (AYJ, AFB), pp. 662–666.
- KDD-2000-DrummondH #representation
- Explicitly representing expected cost: an alternative to ROC representation (CD, RCH), pp. 198–207.