An up-to-date comparison of state-of-the-art classification algorithms
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Xiangliang Zhang | Chongsheng Zhang | Changchang Liu | George Almpanidis | Xiangliang Zhang | Changchang Liu | Chongsheng Zhang | G. Almpanidis
[1] Catherine Blake,et al. UCI Repository of machine learning databases , 1998 .
[2] D. Cox. The Regression Analysis of Binary Sequences , 1958 .
[3] Paolo Giudici,et al. Applied Data Mining for Business and Industry , 2009 .
[4] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[5] Hua Wang,et al. A Comparative Study of Classification Methods For Microarray Data Analysis , 2006, AusDM.
[6] S. García,et al. An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons , 2008 .
[7] John W. Tukey,et al. Exploratory Data Analysis. , 1979 .
[8] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[9] G. Yule. On the Association of Attributes in Statistics: With Illustrations from the Material of the Childhood Society, &c , 1900 .
[10] J. Friedman. Stochastic gradient boosting , 2002 .
[11] Loris Nanni,et al. High performance set of PseAAC and sequence based descriptors for protein classification. , 2010, Journal of theoretical biology.
[12] David Johnstone,et al. An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes , 2015 .
[13] Seetha Hari,et al. Learning From Imbalanced Data , 2019, Advances in Computer and Electrical Engineering.
[14] Rasmus Berg Palm,et al. Prediction as a candidate for learning deep hierarchical models of data , 2012 .
[15] Ivan Bratko,et al. Information-Based Evaluation Criterion for Classifier's Performance , 1991, Machine Learning.
[16] Wen Hu,et al. Real-time classification via sparse representation in acoustic sensor networks , 2013, SenSys '13.
[17] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[18] I. Bratko,et al. Information-based evaluation criterion for classifier's performance , 2004, Machine Learning.
[19] Huan Liu,et al. Feature Selection for Classification , 1997, Intell. Data Anal..
[20] Hongming Zhou,et al. Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[21] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[22] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[23] Michifumi Yoshioka,et al. Recommendation from access logs with ensemble learning , 2017, Artificial Life and Robotics.
[24] Li Xiu,et al. Application of data mining techniques in customer relationship management: A literature review and classification , 2009, Expert Syst. Appl..
[25] Haibo He,et al. Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.
[26] Jean Carletta,et al. Assessing Agreement on Classification Tasks: The Kappa Statistic , 1996, CL.
[27] Luís Torgo,et al. OpenML: A Collaborative Science Platform , 2013, ECML/PKDD.
[28] Yi Chang,et al. Yahoo! Learning to Rank Challenge Overview , 2010, Yahoo! Learning to Rank Challenge.
[29] M. Braga,et al. Exploratory Data Analysis , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..
[30] David J. Hand,et al. A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems , 2001, Machine Learning.
[31] David G. Stork,et al. Pattern Classification , 1973 .
[32] Robert P. W. Duin,et al. Approximating the multiclass ROC by pairwise analysis , 2007, Pattern Recognit. Lett..
[33] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[34] Zijian Zheng,et al. A Benchmark For Classifier Learning , 1993 .
[35] Loris Nanni,et al. Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification , 2015, Comput. Intell. Neurosci..
[36] Hendrik Blockeel,et al. A new way to share, organize and learn from experiments , 2012 .
[37] Paolo Giudici,et al. Applied Data Mining: Statistical Methods for Business and Industry , 2003 .
[38] W. W. Daniel. Applied Nonparametric Statistics , 1979 .
[39] Xindong Wu,et al. Error Detection and Impact-Sensitive Instance Ranking in Noisy Datasets , 2004, AAAI.
[40] Rich Caruana,et al. An empirical comparison of supervised learning algorithms , 2006, ICML.
[41] David J. Hand,et al. Measuring classifier performance: a coherent alternative to the area under the ROC curve , 2009, Machine Learning.
[42] Yufei Xia,et al. A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring , 2017, Expert Syst. Appl..
[43] Mahesh Pal,et al. Random forest classifier for remote sensing classification , 2005 .
[44] Michel Ballings,et al. Evaluating multiple classifiers for stock price direction prediction , 2015, Expert Syst. Appl..
[45] Yvan Saeys,et al. Robust Feature Selection Using Ensemble Feature Selection Techniques , 2008, ECML/PKDD.
[46] Johan A. K. Suykens,et al. Benchmarking state-of-the-art classification algorithms for credit scoring , 2003, J. Oper. Res. Soc..
[47] Loris Nanni,et al. Matrix representation in pattern classification , 2012, Expert Syst. Appl..
[48] Luís Torgo,et al. OpenML: A Collaborative Science Platform , 2013, ECML/PKDD.
[49] Han Tong Loh,et al. Comparison of Extreme Learning Machine with Support Vector Machine for Text Classification , 2005, IEA/AIE.
[50] Chih-Jen Lin,et al. A Practical Guide to Support Vector Classication , 2008 .
[51] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[52] Andrew P. Bradley,et al. The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..
[53] O. J. Dunn. Multiple Comparisons Using Rank Sums , 1964 .
[54] Wei-Yin Loh,et al. A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms , 2000, Machine Learning.
[55] Ying Liu,et al. A Comparative Study on Feature Selection Methods for Drug Discovery , 2004, J. Chem. Inf. Model..
[56] J. Suykens,et al. Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research , 2015, Eur. J. Oper. Res..
[57] María José del Jesús,et al. KEEL: a software tool to assess evolutionary algorithms for data mining problems , 2008, Soft Comput..
[58] Peter A. Flach,et al. A Unified View of Performance Metrics: Translating Threshold Choice into Expected Classification Loss C` Esar Ferri , 2012 .
[59] Núria Macià,et al. Towards UCI+: A mindful repository design , 2014, Inf. Sci..
[60] Geoff Holmes,et al. Experiment databases , 2012, Machine Learning.
[61] M. Friedman. The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .
[62] Christophe Mues,et al. An experimental comparison of classification algorithms for imbalanced credit scoring data sets , 2012, Expert Syst. Appl..
[63] David H. Wolpert,et al. The Lack of A Priori Distinctions Between Learning Algorithms , 1996, Neural Computation.
[64] F. Wilcoxon. Individual Comparisons by Ranking Methods , 1945 .
[65] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[66] Jesús Alcalá-Fdez,et al. KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework , 2011, J. Multiple Valued Log. Soft Comput..
[67] Hsuan-Tien Lin. A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods , 2005 .
[68] Senén Barro,et al. Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..
[69] Azuraliza Abu Bakar,et al. A review of feature selection techniques in sentiment analysis , 2019, Intell. Data Anal..
[70] Cao Feng,et al. STATLOG: COMPARISON OF CLASSIFICATION ALGORITHMS ON LARGE REAL-WORLD PROBLEMS , 1995 .
[71] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[72] Ludmila I. Kuncheva,et al. Switching between selection and fusion in combining classifiers: an experiment , 2002, IEEE Trans. Syst. Man Cybern. Part B.
[73] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[74] S. Cessie,et al. Ridge Estimators in Logistic Regression , 1992 .
[75] San Cristóbal Mateo,et al. The Lack of A Priori Distinctions Between Learning Algorithms , 1996 .
[76] N. Altman. An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .
[77] Huan Liu,et al. Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.
[78] José Manuel Benítez,et al. Empirical study of feature selection methods based on individual feature evaluation for classification problems , 2011, Expert Syst. Appl..
[79] Zhaohui Zheng,et al. Stochastic gradient boosted distributed decision trees , 2009, CIKM.
[80] Dong Yu,et al. Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..
[81] Charles X. Ling,et al. AUC: A Better Measure than Accuracy in Comparing Learning Algorithms , 2003, Canadian Conference on AI.
[82] Allen Y. Yang,et al. Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[83] André Carlos Ponce de Leon Ferreira de Carvalho,et al. Comparing machine learning classifiers in potential distribution modelling , 2011, Expert Syst. Appl..
[84] Philip S. Yu,et al. Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.
[85] Ludmila I Kuncheva,et al. Classifier ensembles for fMRI data analysis: an experiment. , 2010, Magnetic resonance imaging.
[86] Loris Nanni,et al. Coupling different methods for overcoming the class imbalance problem , 2015, Neurocomputing.
[87] Pedro Larrañaga,et al. A review of feature selection techniques in bioinformatics , 2007, Bioinform..
[88] Tao Li,et al. A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression , 2004, Bioinform..
[89] Taghi M. Khoshgoftaar,et al. An Empirical Study of Learning from Imbalanced Data Using Random Forest , 2007, 19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007).
[90] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[91] Chih-Jen Lin,et al. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.