Reducing the number of trees in a forest using noisy features

• Adnan MN, Islam MZ (2016) Optimizing the number of trees in a decision forest to discover a subforest with high ensemble accuracy using a genetic algorithm. Knowl-Based Syst 110:86–97

  Article  Google Scholar 

• Adnan MN, Islam MZ et al (2017a) Forex++: a new framework for knowledge discovery from decision forests. Australas J Inf Syst 21

• Adnan MN, Islam MZ (2017b) Forest pa: constructing a decision forest by penalizing attributes used in previous trees. Expert Syst Appl 89:389–403

  Article  Google Scholar 

• Akhiat Y, Chahhou M, Zinedine A (2018) Feature selection based on graph representation. In: 2018 IEEE 5th international congress on Information science and technology (CiSt). IEEE, pp 232–237

• Akhiat Y, Chahhou M, Zinedine A (2019) Ensemble feature selection algorithm. Int J Intell Syst Appl 11(1):24

  Google Scholar 

• Akhiat Y, Asnaoui Y, Chahhou M, Zinedine A (2021a) A new graph feature selection approach. In: 2020 6th IEEE congress on information science and technology (CiSt). IEEE, pp 156–161

• Akhiat Y, Manzali Y, Chahhou M, Zinedine A (2021b) A new noisy random forest based method for feature selection. Cybern Inf Technol 21(2)

• Ali KM, Pazzani MJ (1996) Error reduction through learning multiple descriptions. Mach Learn 24(3):173–202

  Article  Google Scholar 

• Angelov PP, Gu X (2019) Empirical approach to machine learning. Springer, Berlin

  Book  Google Scholar 

• Angelov PP, Gu X, Príncipe JC (2017) A generalized methodology for data analysis. IEEE Trans Cybern 48(10):2981–2993

  Article  Google Scholar 

• Bernard S, Heutte L, Adam S (2008) Forest-rk: a new random forest induction method. In: International conference on intelligent computing. Springer, pp 430–437

• Bernard S, Heutte L, Adam S (2009) On the selection of decision trees in random forests. In: 2009 international joint conference on neural networks. IEEE, pp 302–307

• Bernard S, Adam S, Heutte L (2012) Dynamic random forests. Pattern Recogn Lett 33(12):1580–1586

  Article  Google Scholar 

• Breiman L (2001) Random forests. Mach Learn 45(1):5–32

  Article  MATH  Google Scholar 

• Brieman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees, vol 67. Wadsworth Inc, Routledge

  Google Scholar 

• Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

  Article  MATH  Google Scholar 

• Daho MEH, Settouti N, Bechar MEA, Boublenza A, Chikh MA (2021) A new correlation-based approach for ensemble selection in random forests. Int J Intell Comput Cybern 14(2):251–268

• Deng H (2019) Interpreting tree ensembles with in trees. Int J Data Sci Anal 7(4):277–287

  Article  Google Scholar 

• Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367–378

  Article  MathSciNet  MATH  Google Scholar 

• Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63(1):3–42

  Article  MATH  Google Scholar 

• Guo H, Liu H, Li R, Wu C, Guo Y, Xu M (2018) Margin & diversity based ordering ensemble pruning. Neurocomputing 275:237–246

  Article  Google Scholar 

• Hart PE, Stork DG, Duda RO (2000) Pattern classification. Wiley, Hoboken

  MATH  Google Scholar 

• Huang G-B, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybern) 42(2):513–529

  Article  Google Scholar 

• Jiang X, Wu C-A, Guo H (2017) Forest pruning based on branch importance. Comput Intell Neurosci  2017

• Keller JM, Gray MR, Givens JA (1985) A fuzzy k-nearest neighbor algorithm. IEEE Trans Syst Man Cybern 4:580–585

  Article  Google Scholar 

• Khan Z, Gul A, Mahmoud O, Miftahuddin M, Perperoglou A, Adler W, Lausen B (2016) An ensemble of optimal trees for class membership probability estimation. In: Analysis of large and complex data. Springer, pp 395–409

• Khan Z, Gul A, Perperoglou A, Miftahuddin M, Mahmoud O, Adler W, Lausen B (2020) Ensemble of optimal trees, random forest and random projection ensemble classification. Adv Data Anal Classif 14(1):97–116

  Article  MathSciNet  MATH  Google Scholar 

• Khan Z, Gul N, Faiz N, Gul A, Adler W, Lausen B (2021) Optimal trees selection for classification via out-of-bag assessment and sub-bagging. IEEE Access 9:28591–28607

  Article  Google Scholar 

• Kulkarni V, Sinha P, Singh A (2012) Heuristic based improvements for effective random forest classifier. In: Proceedings of international conference on computational intelligence. Springer, Chennai

• Latinne P, Debeir O, Decaestecker C (2001) Limiting the number of trees in random forests. In: International workshop on multiple classifier systems. Springer, pp 178–187

• Lichman M et al (2013) Uci machine learning repository

• Lu Z, Wu X, Zhu X, Bongard J (2010) Ensemble pruning via individual contribution ordering. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 871–880

• Maclin R, Opitz D (2011) Popular ensemble methods: an empirical study. J Artif Intell Res. arXiv:1106.0257

• Margineantu DD, Dietterich TG (1997) Pruning adaptive boosting. In: ICML, vol 97. Citeseer, pp. 211–218

• Martınez-Munoz G, Suárez A (2004) Aggregation ordering in bagging. In: Proc. of the IASTED international conference on artificial intelligence and applications. Citeseer, pp 258–263

• Nan F, Wang J, Saligrama V (2016) Pruning random forests for prediction on a budget. In: Advances in neural information processing systems, pp 2334–2342

• Oh D-Y (2012) GA-Boost: a genetic algorithm for robust boosting. The University of Alabama

• Ordóñez FJ, Ledezma A, Sanchis A (2008) Genetic approach for optimizing ensembles of classifiers. In: FLAIRS conference, pp 89–94

• Oshiro TM, Perez PS, Baranauskas JA (2012) How many trees in a random forest? In: International workshop on machine learning and data mining in pattern recognition. Springer, pp 154–168

• Rakers C, Reker D, Brown JB (2017) Small random forest models for effective chemogenomic active learning. J Comput Aided Chem 18:124–142

  Article  Google Scholar 

• Rodríguez-Fdez I, Canosa A, Mucientes M, Bugarín A (2015) STAC: a web platform for the comparison of algorithms using statistical tests. In: Proceedings of the 2015 IEEE international conference on Fuzzy systems (FUZZ-IEEE)

• Ruta D, Gabrys B (2005) Classifier selection for majority voting. Inf Fusion 6(1):63–81

  Article  MATH  Google Scholar 

• Souad TZ, Abdelkader A (2019) Pruning of random forests: a diversity-based heuristic measure to simplify a random forest ensemble. INFOCOMP: J Comput Sci 18(1)

• Tripoliti EE, Fotiadis DI, Manis G (2010) Dynamic construction of random forests: evaluation using biomedical engineering problems. In: Proceedings of the 10th IEEE international conference on information technology and applications in biomedicine. IEEE, pp 1–4

• Wang Y, Wang D, Geng N, Wang Y, Yin Y, Jin Y (2019) Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection. Appl Soft Comput 77:188–204

  Article  Google Scholar 

• Xu X, Chen W (2017) Implementation and performance optimization of dynamic random forest. In: 2017 International conference on cyber-enabled distributed computing and knowledge discovery (CyberC). IEEE, pp 283–289

• Yang F, Lu W-H, Luo L-K, Li T (2012) Margin optimization based pruning for random forest. Neurocomputing 94:54–63

  Article  Google Scholar 

• Yassine A, Mohamed C, Zinedine A (2017) Feature selection based on pairwise evalution. In: 2017 intelligent systems and computer vision (ISCV). IEEE, pp 1–6

• Ying X (2019) An overview of overfitting and its solutions. J Phys: Conf Ser 1168:022022

  Google Scholar 

• Zhang H, Wang M (2009) Search for the smallest random forest. Stat Interface 2(3):381

  Article  MathSciNet  MATH  Google Scholar 

• Zhang C, Liu C, Zhang X, Almpanidis G (2017) An up-to-date comparison of state-of-the-art classification algorithms. Expert Syst Appl 82:128–150

  Article  Google Scholar 

Download references