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1
Is Combining Classifiers with Stacking Better than Selecting the Best One?
Is Combining Classifiers with Stacking Better than Selecting the Best One?
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Is Combining Classifiers with Stacking Better than Selecting the Best One?

Machine learning, 2004-03, Vol.54 (3), p.255-273 [Peer Reviewed Journal]

Kluwer Academic Publishers 2004 ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1023/B:MACH.0000015881.36452.6e

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2
Prediction of linear B-cell epitopes using amino acid pair antigenicity scale
Prediction of linear B-cell epitopes using amino acid pair antigenicity scale
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Prediction of linear B-cell epitopes using amino acid pair antigenicity scale

Amino acids, 2007-09, Vol.33 (3), p.423-428 [Peer Reviewed Journal]

Springer-Verlag 2007 ;ISSN: 0939-4451 ;EISSN: 1438-2199 ;DOI: 10.1007/s00726-006-0485-9 ;PMID: 17252308

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3
SMOTE: Synthetic Minority Over-sampling Technique
SMOTE: Synthetic Minority Over-sampling Technique
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SMOTE: Synthetic Minority Over-sampling Technique

The Journal of artificial intelligence research, 2002-01, Vol.16, p.321-357 [Peer Reviewed Journal]

2002. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.jair.org/index.php/jair/about ;ISSN: 1076-9757 ;EISSN: 1943-5037 ;DOI: 10.1613/jair.953

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4
Domain Adaptation for Statistical Classifiers
Domain Adaptation for Statistical Classifiers
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Domain Adaptation for Statistical Classifiers

The Journal of artificial intelligence research, 2006-01, Vol.26, p.101-126 [Peer Reviewed Journal]

2006. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.jair.org/index.php/jair/about ;ISSN: 1076-9757 ;EISSN: 1943-5037 ;DOI: 10.1613/jair.1872

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5
Decision templates for multiple classifier fusion: an experimental comparison
Decision templates for multiple classifier fusion: an experimental comparison
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Decision templates for multiple classifier fusion: an experimental comparison

Pattern recognition, 2001-02, Vol.34 (2), p.299-314 [Peer Reviewed Journal]

2001 Pattern Recognition Society ;ISSN: 0031-3203 ;EISSN: 1873-5142 ;DOI: 10.1016/S0031-3203(99)00223-X

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6
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
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Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy

Machine learning, 2003-05, Vol.51 (2), p.181-207 [Peer Reviewed Journal]

Kluwer Academic Publishers 2003 ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1023/A:1022859003006

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7
Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings
Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings
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Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings

IEEE transactions on software engineering, 2008-07, Vol.34 (4), p.485-496 [Peer Reviewed Journal]

Copyright IEEE Computer Society Apr 2008 ;ISSN: 0098-5589 ;EISSN: 1939-3520 ;DOI: 10.1109/TSE.2008.35 ;CODEN: IESEDJ

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8
Learning classifier systems resources
Learning classifier systems resources
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Learning classifier systems resources

Soft computing (Berlin, Germany), 2002-06, Vol.6 (3-4), p.240-243 [Peer Reviewed Journal]

Springer-Verlag Berlin Heidelberg 2002. ;ISSN: 1432-7643 ;EISSN: 1433-7479 ;DOI: 10.1007/s005000100119

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9
Comment on “On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes”
Comment on “On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes”
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Comment on “On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes”

Neural processing letters, 2008-12, Vol.28 (3), p.169-187 [Peer Reviewed Journal]

Springer Science+Business Media, LLC. 2008 ;2009 INIST-CNRS ;Springer Science+Business Media, LLC. 2008. ;ISSN: 1370-4621 ;EISSN: 1573-773X ;DOI: 10.1007/s11063-008-9088-7

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10
Combining multiple classifiers by averaging or by multiplying?
Combining multiple classifiers by averaging or by multiplying?
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Combining multiple classifiers by averaging or by multiplying?

Pattern recognition, 2000-09, Vol.33 (9), p.1475-1485 [Peer Reviewed Journal]

2000 Pattern Recognition Society ;ISSN: 0031-3203 ;EISSN: 1873-5142 ;DOI: 10.1016/S0031-3203(99)00138-7

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11
Subclass problem-dependent design for error-correcting output codes
Subclass problem-dependent design for error-correcting output codes
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Subclass problem-dependent design for error-correcting output codes

IEEE transactions on pattern analysis and machine intelligence, 2008-06 [Peer Reviewed Journal]

(c) IEEE, 2008 info:eu-repo/semantics/openAccess ;ISSN: 0162-8828 ;EISSN: 1939-3539 ;DOI: 10.1109/TPAMI.2008.38

Digital Resources/Online E-Resources

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12
Machine learning: a review of classification and combining techniques
Machine learning: a review of classification and combining techniques
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Machine learning: a review of classification and combining techniques

The Artificial intelligence review, 2006-11, Vol.26 (3), p.159-190 [Peer Reviewed Journal]

Springer Science+Business Media B.V. 2007 ;ISSN: 0269-2821 ;EISSN: 1573-7462 ;DOI: 10.1007/s10462-007-9052-3

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13
Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction
Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction
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Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction

Amino acids, 2007-07, Vol.33 (1), p.57-67 [Peer Reviewed Journal]

Springer-Verlag 2007. ;ISSN: 0939-4451 ;EISSN: 1438-2199 ;DOI: 10.1007/s00726-006-0478-8 ;PMID: 17235453

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14
Combining Classifiers with Meta Decision Trees
Combining Classifiers with Meta Decision Trees
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Combining Classifiers with Meta Decision Trees

Machine learning, 2003-03, Vol.50 (3), p.223-249 [Peer Reviewed Journal]

Kluwer Academic Publishers 2003 ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1023/A:1021709817809

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15
Popular Ensemble Methods: An Empirical Study
Popular Ensemble Methods: An Empirical Study
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Popular Ensemble Methods: An Empirical Study

The Journal of artificial intelligence research, , Vol.11, p.169-198 [Peer Reviewed Journal]

1999. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.jair.org/index.php/jair/about ;ISSN: 1076-9757 ;EISSN: 1943-5037 ;DOI: 10.1613/jair.614

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16
Classifying Software Changes: Clean or Buggy?
Classifying Software Changes: Clean or Buggy?
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Classifying Software Changes: Clean or Buggy?

IEEE transactions on software engineering, 2008-03, Vol.34 (2), p.181-196 [Peer Reviewed Journal]

Copyright IEEE Computer Society Mar 2008 ;ISSN: 0098-5589 ;EISSN: 1939-3520 ;DOI: 10.1109/TSE.2007.70773 ;CODEN: IESEDJ

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17
Bagging, Boosting and the Random Subspace Method for Linear Classifiers
Bagging, Boosting and the Random Subspace Method for Linear Classifiers
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Bagging, Boosting and the Random Subspace Method for Linear Classifiers

Pattern analysis and applications : PAA, , Vol.5 (2), p.121-135 [Peer Reviewed Journal]

ISSN: 1433-7541 ;EISSN: 1433-755X ;DOI: 10.1007/s100440200011

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18
Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction
Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction
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Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction

The Journal of artificial intelligence research, 2003-01, Vol.19, p.315-354 [Peer Reviewed Journal]

2003. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.jair.org/index.php/jair/about ;ISSN: 1076-9757 ;EISSN: 1943-5037 ;DOI: 10.1613/jair.1199

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19
Using ensemble classifier to identify membrane protein types
Using ensemble classifier to identify membrane protein types
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Using ensemble classifier to identify membrane protein types

Amino acids, 2007-05, Vol.32 (4), p.483-488 [Peer Reviewed Journal]

Springer-Verlag 2007 ;ISSN: 0939-4451 ;EISSN: 1438-2199 ;DOI: 10.1007/s00726-006-0439-2 ;PMID: 17031474

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20
Cost curves : An improved method for visualizing classifier performance
Cost curves : An improved method for visualizing classifier performance
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Cost curves : An improved method for visualizing classifier performance

Machine learning, 2006-10, Vol.65 (1), p.95-130 [Peer Reviewed Journal]

2007 INIST-CNRS ;Springer Science + Business Media, LLC 2006 ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-006-8199-5

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