Fetching the paper…
Reading the bibliography…
Characteristics extracted from the training datasets of classification problems have proven to be effective predictors in a number of meta-analyses.
Pattern classifier design by linear programming
Fred W Smith · 1968
Earlier work this paper cites.
Perceptrons: An Introduction to Computational Geometry
Marvin Minsky and Seymour Papert · 1969
Earlier work this paper cites.
A general coefficient of similarity and some of its properties
John Gower · 1971
Earlier work this paper cites.
A dendrite method for cluster analysis
Tadeusz Caliński and Jerzy Harabasz · 1974
Earlier work this paper cites.
An Introduction to Kolmogorov Complexity and Its Applications
Li Ming and Paul Vitanyi · 1993
Earlier work this paper cites.
On the nonlinearity of pattern classifiers
Aarnoud Hoekstra and Robert P W Duin · 1996
Earlier work this paper cites.
Pretopological approach for supervised learning
Frank Lebourgeois and Hubert Emptoz · 1996
Earlier work this paper cites.
The lack of a priori distinctions between learning algorithms
David H Wolpert · 1996
Earlier work this paper cites.
Improved heterogeneous distance functions
D Randall Wilson and Tony R Martinez · 1997
Earlier work this paper cites.
An introduction to support vector machines and other kernel-based learning methods
Nello Cristianini and John Shawe-Taylor · 2000
Earlier work this paper cites.
Complexity of classification problems and comparative advantages of combined classifiers
Tin K Ho · 2000
Earlier work this paper cites.
Two-parameter fisher criterion
Witold Malina · 2001
Earlier work this paper cites.
A data complexity analysis of comparative advantages of decision forest constructors
Tin K Ho · 2002
Earlier work this paper cites.
Complexity measures of supervised classification problems
Tin K Ho and Mitra Basu · 2002
Earlier work this paper cites.
A perspective view and survey of meta-learning
Ricardo Vilalta and Youssef Drissi · 2002
Earlier work this paper cites.
Feature subset selection using a new definition of classificability
Ming Dong and Rishabh P Kothari · 2003
Earlier work this paper cites.
A study of the behavior of several methods for balancing machine learning training data
Gustavo E A P A Batista, Ronaldo C Prati, and Maria C Monard · 2004
Earlier work this paper cites.
Domain of competence of xcs classifier system in complexity measurement space
Ester Bernadó-Mansilla and Tin K Ho · 2005
Earlier work this paper cites.
Data characterization for effective prototype selection
Ramón A Mollineda, José S Sánchez, and José M Sotoca · 2005
Earlier work this paper cites.
A review of data complexity measures and their applicability to pattern classification problems
José M Sotoca, José Sánchez, and Ramón A Mollineda · 2005
Earlier work this paper cites.
Semi-supervised learning with graphs
Xiaojin Zhu, John Lafferty, and Ronald Rosenfeld · 2005
Earlier work this paper cites.
On learning algorithm selection for classification
Shawkat Ali and Kate A Smith · 2006
Earlier work this paper cites.
Data complexity in pattern recognition
Mitra Basu and Tin K Ho · 2006
Earlier work this paper cites.
Complexity of magnetic resonance spectrum classification
Richard Baumgartner, Tin K Ho, Ray Somorjai, Uwe Himmelreich, and Tania Sorrell · 2006
Earlier work this paper cites.
Classifier domains of competence in data complexity space
Tin K Ho and Ester Bernadó-Mansilla · 2006
Earlier work this paper cites.
Measures of geometrical complexity in classification problems
Tin K Ho, Mitra Basu, and Martin H C Law · 2006
Earlier work this paper cites.
Discretization techniques: A recent survey
Sotiris Kotsiantis and Dimitris Kanellopoulos · 2006
Earlier work this paper cites.
Data complexity in machine learning
Li Ling and Yaser S Abu-Mostafa · 2006
Earlier work this paper cites.
A meta-learning framework for pattern classification by means of data complexity measures
Ramón A Mollineda, José S Sánchez, and José M Sotoca · 2006
Earlier work this paper cites.
Class separability in spaces reduced by feature selection
Erinija Pranckeviciene, Tin K Ho, and Ray Somorjai · 2006
Earlier work this paper cites.
Support vector machine solvers
Léon Bottou and Chih-Jen Lin · 2007
Earlier work this paper cites.
A methodology for analyzing case retrieval from a clustered case memory
Albert Fornells, Elisabet Golobardes, Josep M Martorell, Josep M Garrell, Núria Macià, and Ester Bernadó · 2007
Earlier work this paper cites.
Measures for the characterisation of pattern-recognition data sets
Christiaan V D Walt and Etienne Barnard · 2007
Earlier work this paper cites.
Toward a measure of classification complexity in gene expression signatures
Vidya Kamath, Timothy J Yeatman, and Steven A Eschrich · 2008
Earlier work this paper cites.
A review on the combination of binary classifiers in multiclass problems
Ana C Lorena, André C P L F de Carvalho, and João M P Gama · 2008
Earlier work this paper cites.
Preliminary approach on synthetic data sets generation based on class separability measure
Núria Macia, Ester Bernadó-Mansilla, and Albert Orriols-Puig · 2008
Cited alongside, same era.
A note on the separability index
Linda Mthembu and Tshilidzi Marwala · 2008
Cited alongside, same era.
Using supervised complexity measures in the analysis of cancer gene expression data sets
Ivan G Costa, Ana C Lorena, Liciana R M P y Peres, and Marcilio C P de Souto · 2009
Cited alongside, same era.
Learning from imbalanced data
Haibo He and Edwardo A Garcia · 2009
Cited alongside, same era.
On using prototype reduction schemes to enhance the computation of volume-based inter-class overlap measures
Sang-Woon Kim and John Oommen · 2009
Cited alongside, same era.
Measurement of data complexity for classification problems with unbalanced data
Nafees Anwar, Geoff Jones, and Siva Ganesh · 2014
Later among the works it cites.
Dynamic selection of classifiers - a comprehensive review
Alceu S Britto Jr, Robert Sabourin, and Luiz E S Oliveira · 2014
Later among the works it cites.
Domains of competence of the semi-naive bayesian network classifiers
María J Flores, José A Gámez, and Ana M Martínez · 2014
Later among the works it cites.
Classification in the presence of label noise: a survey
Benoit Frenay and Michel Verleysen · 2014
Later among the works it cites.
A set of complexity measures designed for applying meta-learning to instance selection
Enrique Leyva, Antonio González, and Raúl Pérez · 2014
Later among the works it cites.
Towards uci+: A mindful repository design
Núria Macià and Ester Bernadó-Mansilla · 2014
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Eric D Kolaczyk · 2009
Cited alongside, same era.
Cross-disciplinary perspectives on meta-learning for algorithm selection
Kate A Smith-Miles · 2009
Cited alongside, same era.
Selecting discrete and continuous features based on neighborhood decision error minimization
Qinghua Hu, Witold Pedrycz, Daren Yu, and Jun Lang · 2010
Cited alongside, same era.
Feature selection: An ever evolving frontier in data mining
Huan Liu, Hiroshi Motoda, Rudy Setiono, and Zheng Zhao · 2010
Cited alongside, same era.
Building binary-tree-based multiclass classifiers using separability measures
Ana C Lorena and André C P L F de Carvalho · 2010
Cited alongside, same era.
In search of targeted-complexity problems
Núria Macià, Albert Orriols-Puig, and Ester Bernadó-Mansilla · 2010
Cited alongside, same era.
Documentation for the data complexity library in c++
Albert Orriols-Puig, Núria Macià, and Tin K Ho · 2010
Cited alongside, same era.
Later among the works it cites.
A direct measure of discriminant and characteristic capability for classifier building and assessment
Giuliano Armano · 2015
Later among the works it cites.
META-DES: A dynamic ensemble selection framework using meta-learning
Rafael M O Cruz, Robert Sabourin, George D C Cavalcanti, and Tsang Ing Ren · 2015
Later among the works it cites.
Effect of label noise in the complexity of classification problems
Luís P F Garcia, André C P L F de Carvalho, and Ana C Lorena · 2015
Later among the works it cites.
Quantification of side-channel information leaks based on data complexity measures for web browsing
Zhi-Min He, Patrick P K Chan, Daniel S Yeung, Witold Pedrycz, and Wing W Y Ng · 2015
Later among the works it cites.
An automatic extraction method of the domains of competence for learning classifiers using data complexity measures
Julián Luengo and Francisco Herrera · 2015
Later among the works it cites.
Experimenting multiresolution analysis for identifying regions of different classification complexity
Giuliano Armano and Emanuele Tamponi · 2016
Later among the works it cites.
On the impact of dataset complexity and sampling strategy in multilabel classifiers performance
Francisco Charte, Antonio Rivera, María J del Jesus, and Francisco Herrera · 2016
Later among the works it cites.
A meta-learning framework for algorithm recommendation in software fault prediction
Silvia N das Dôres, Luciano Alves, Duncan D Ruiz, and Rodrigo C Barros · 2016
Later among the works it cites.
Noise detection in the meta-learning level
Luís P F Garcia, André C P L F de Carvalho, and Ana C Lorena · 2016
Later among the works it cites.
The effects of model and data complexity on predictions from species distributions models
David García-Callejas and Miguel B Araújo · 2016
Later among the works it cites.
Meta-learning recommendation of default size of classifier pool for META-DES
Anandarup Roy, Rafael M O Cruz, Robert Sabourin, and George D C Cavalcanti · 2016
Later among the works it cites.
An initial study on the rank of input matrix for extreme learning machine
Xingmin Zhao, Weipeng Cao, Hongyu Zhu, Zhong Ming, and Rana Aamir Raza Ashfaq · 2016
Later among the works it cites.
Complexity curve: a graphical measure of data complexity and classifier performance
Julian Zubek and Dariusz M Plewczynski · 2016
Later among the works it cites.
Analyzing the behavior of aggregation and pre-aggregation functions in fuzzy rule-based classification systems with data complexity measures
Giancarlo Lucca, Jose Sanz, Graçaliz P Dimuro, Benjamín Bedregal, and Humberto Bustince · 2017
Later among the works it cites.
Complexity measures effectiveness in feature selection
Lucas Chesini Okimoto, Ricardo Manhães Savii, and Ana Carolina Lorena · 2017
Later among the works it cites.
Metalearning for choosing feature selection algorithms in data mining: Proposal of a new framework
Antonio R S Parmezan, Huei D Lee, and Feng C Wu · 2017
Later among the works it cites.
A framework for dynamic classifier selection oriented by the classification problem difficulty
André L Brun, Alceu S Britto Jr, Luiz S Oliveira, Fabricio Enembreck, and Robert Sabourin · 2018
Closest in time.
Dynamic classifier selection: Recent advances and perspectives
Rafael M O Cruz, Robert Sabourin, and George D C Cavalcanti · 2018
Closest in time.
Using complexity measures to evolve synthetic classification datasets
Vinícius V de Melo and Ana C Lorena · 2018
Closest in time.
Learning from imbalanced data sets
Alberto Fernández, Salvador García, Mikel Galar, Ronaldo C Prati, Bartosz Krawczyk, and Francisco Herrera · 2018
Closest in time.
Classifier recommendation using data complexity measures
Luís P F Garcia, Ana C Lorena, Marcilio C P de Souto, and Tin Kam Ho · 2018
Closest in time.
Data complexity meta-features for regression problems
Ana C Lorena, Aron I Maciel, Pericles B C Miranda, Ivan G Costa, and Ricardo B C Prudêncio · 2018
Closest in time.
Instance spaces for machine learning classification
Mario A Muñoz, Laura Villanova, Davaatseren Baatar, and Kate Smith-Miles · 2018
Closest in time.
Using complexity measures to determine the structure of directed acyclic graphs in multiclass classification
Thaise M Quiterio and Ana C Lorena · 2018
Closest in time.
Cross-validation for imbalanced datasets: Avoiding overoptimistic and overfitting approaches
Miriam Seoane Santos, Jastin Pompeu Soares, Pedro Henrigues Abreu, Helder Araujo, and Joao Santos · 2018
Closest in time.
Analyzing data complexity using metafeatures for classification algorithm selection
Rushit Shah, Varun Khemani, Michael Azarian, Michael Pecht, and Yan Su · 2018
Closest in time.
On developing an automatic threshold applied to feature selection ensembles
Borja Seijo-Pardo, Verónica Bolón-Canedo, and Amparo Alonso-Betanzos · 2019
Closest in time.
A novel ECOC algorithm for multiclass microarray data classification based on data complexity analysis
MengXin Sun, KunHong Liu, QingQiang Wu, QingQi Hong, BeiZhan Wang, and Haiying Zhang · 2019
Closest in time.
An instance-based learning recommendation algorithm of imbalance handling methods
Xueying Zhang, Ruixian Li, Bo Zhang, Yunxiang Yang, Jing Guo, and Xiang Ji · 2019
Closest in time.