Angelis, L., & Stamelos, I. (2000). A simulation tool for efficient analogy based cost estimation. Empirical Software Engineering, 5(1), 35–68. https://doi.org/10.1023/A:1009897800559 .
Auer, M., Trendowicz, A., Graser, B., Haunschmid, E., & Biffl, S. (2006). Optimal project feature weights in analogy-based cost estimation: improvement and limitations. Software Engineering, IEEE Transactions on, 32(2), 83–92.
Awada, W., Khoshgoftaar, T. M., Dittman, D., Wald, R., Napolitano, A. (2012). A review of the stability of feature selection techniques for bioinformatics data. In 2012 I.E. 13th International Conference on Information Reuse and Integration (IRI) (pp. 356–363). Presented at the 2012 I.E. 13th International Conference on Information Reuse and Integration (IRI). https://doi.org/10.1109/IRI.2012.6303031 .
[+]
Angelis, L., & Stamelos, I. (2000). A simulation tool for efficient analogy based cost estimation. Empirical Software Engineering, 5(1), 35–68. https://doi.org/10.1023/A:1009897800559 .
Auer, M., Trendowicz, A., Graser, B., Haunschmid, E., & Biffl, S. (2006). Optimal project feature weights in analogy-based cost estimation: improvement and limitations. Software Engineering, IEEE Transactions on, 32(2), 83–92.
Awada, W., Khoshgoftaar, T. M., Dittman, D., Wald, R., Napolitano, A. (2012). A review of the stability of feature selection techniques for bioinformatics data. In 2012 I.E. 13th International Conference on Information Reuse and Integration (IRI) (pp. 356–363). Presented at the 2012 I.E. 13th International Conference on Information Reuse and Integration (IRI). https://doi.org/10.1109/IRI.2012.6303031 .
Battiti, R. (1994). Using mutual information for selecting features in supervised neural net learning. Neural Networks, IEEE Transactions, 5(4), 537–550.
Bennasar, M., Hicks, Y., & Setchi, R. (2015). Feature selection using joint mutual information maximisation. Expert Systems with Applications, 42(22), 8520–8532. https://doi.org/10.1016/j.eswa.2015.07.007 .
Bibi, S., Tsoumakas, G., Stamelos, I., & Vlahavas, I. (2008). Regression via classification applied on software defect estimation. Expert Systems with Applications, 34(3), 2091–2101. https://doi.org/10.1016/j.eswa.2007.02.012 .
Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16–28.
Chatzipetrou, P., Papatheocharous, E., Angelis, L., Andreou, A. S. (2012). An investigation of software effort phase distribution using compositional data analysis. In 2012 38th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA) (pp. 367–375). Presented at the 2012 38th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA). https://doi.org/10.1109/SEAA.2012.50 .
Chen, Z., Menzies, T., Port, D., & Boehm, B. (2005). Feature subset selection can improve software cost estimation accuracy. In Proceedings of the 2005 workshop on predictor models in software engineering (pp. 1–6). New York: ACM. https://doi.org/10.1145/1082983.1083171 .
Chiu, N.-H., & Huang, S.-J. (2007). The adjusted analogy-based software effort estimation based on similarity distances. Journal of Systems and Software, 80(4), 628–640.
Dash, M., & Liu, H. (2003). Consistency-based search in feature selection. Artificial Intelligence, 151(1), 155–176.
Dejaeger, K., Verbeke, W., Martens, D., & Baesens, B. (2012). Data mining techniques for software effort estimation: a comparative study. Software Engineering, IEEE Transactions on, 38(2), 375–397. https://doi.org/10.1109/TSE.2011.55 .
Deng, K., & MacDonell, S. G. (2008). Maximising data retention from the ISBSG repository. In Proceedings of the 12th international conference on evaluation and assessment in software engineering (pp. 21–30). Swinton: British Computer Society http://dl.acm.org/citation.cfm?id=2227115.2227118 . Accessed 21 Jan 2014.
Doquire, G., & Verleysen, M. (2011). An hybrid approach to feature selection for mixed categorical and continuous data. In International Conference on Knowledge Discovery and Information Retrieval. http://hdl.handle.net/2078.1/90765 . Accessed 2 Nov 2015.
Dudani, S. A. (1976). The distance-weighted k-nearest-neighbor rule. IEEE Transactions on Systems, Man and Cybernetics, SMC, 6(4), 325–327. https://doi.org/10.1109/TSMC.1976.5408784 .
Estévez, P. A., Tesmer, M., Perez, C. A., & Zurada, J. M. (2009). Normalized mutual information feature selection. IEEE Transactions on Neural Networks, 20(2), 189–201. https://doi.org/10.1109/TNN.2008.2005601 .
Fayyad, U.M., & Irani, K.B. (1993). Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In Proceedings of the International Joint Conference on Uncertainty in AI (pp. 1022–1027). Presented at the International Joint Conference on Uncertainty in AI. https://www.researchgate.net/publication/220815890_Multi-Interval_Discretization_of_Continuous-Valued_Attributes_for_Classification_Learning . Accessed 22 June 2016.
Fernández-Diego, M., & González-Ladrón-de-Guevara, F. (2014). Potential and limitations of the ISBSG dataset in enhancing software engineering research: a mapping review. Information and Software Technology, 56(6), 527–544. https://doi.org/10.1016/j.infsof.2014.01.003 .
Ferreira, A., & Figueiredo, M. (2011). Unsupervised joint feature discretization and selection. In J. Vitrià, J. M. Sanches, & M. Hernández (Eds.), Pattern recognition and image analysis (Vol. 6669, pp. 200–207). Berlin, Heidelberg: Springer Berlin Heidelberg http://link.springer.com/10.1007/978-3-642-21257-4_25 . Accessed 4 Mar 2016.
Fleuret, F. (2004). Fast binary feature selection with conditional mutual information. Journal of Machine Learning Research, 5, 1531–1555.
González-Ladrón-de-Guevara, F., Fernández-Diego, M., & Lokan, C. (2016). The usage of ISBSG data fields in software effort estimation: a systematic mapping study. Journal of Systems and Software, 113, 188–215. https://doi.org/10.1016/j.jss.2015.11.040 .
Gupta, P., Jain, S., & Jain, A. (2014). A review of fast clustering-based feature subset selection algorithm. International Journal of Scientific & Technology Research, 3(11), 86–91.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. The Journal of Machine Learning Research, 3, 1157–1182.
Hall, M. A., & Holmes, G. (2003). Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering, 15(6), 1437–1447. https://doi.org/10.1109/TKDE.2003.1245283 .
Hausser, J., & Strimmer, K. (2009). Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks. Journal of Machine Learning Research, 10(Jul), 1469–1484.
Hill, P. (2010). Practical software project estimation: a toolkit for estimating software development effort & duration. McGraw Hill Professional.
Hsu, H.-H., Hsieh, C.-W., & Lu, M.-D. (2011). Hybrid feature selection by combining filters and wrappers. Expert Systems with Applications, 38(7), 8144–8150.
Huang, S.-J., & Chiu, N.-H. (2006). Optimization of analogy weights by genetic algorithm for software effort estimation. Information and Software Technology, 48(11), 1034–1045. https://doi.org/10.1016/j.infsof.2005.12.020 .
Huang, S.-J., Chiu, N.-H., & Liu, Y.-J. (2008). A comparative evaluation on the accuracies of software effort estimates from clustered data. Information and Software Technology, 50(9–10), 879–888. https://doi.org/10.1016/j.infsof.2008.02.005 .
Huang, J., Li, Y.-F., & Xie, M. (2015). An empirical analysis of data preprocessing for machine learning-based software cost estimation. Information and Software Technology, 67, 108–127. https://doi.org/10.1016/j.infsof.2015.07.004 .
ISBSG. (2013a). ISBSG Dataset Release 12. ISBSG. http://isbsg.org/ . Accessed 1 Mar 2016.
ISBSG. (2013b). ISBSG Guidelines Release 12.
ISBSG. (2013c). ISBSG Data Demographics Release 12.
Jeffery, R., Ruhe, M., Wieczorek, I. (2001). Using public domain metrics to estimate software development effort. In Software Metrics Symposium, 2001. METRICS 2001. Proceedings. Seventh International (pp. 16–27). https://doi.org/10.1109/METRIC.2001.915512 .
Jiang, Z., & Comstock, C. (2007). The factors significant to software development productivity. In C. Ardil (Ed.), Proceedings of World Academy of Science, Engineering and Technology, Vol 19 (Vol. 19, pp. 160–164). Presented at the Conference of the World-Academy-of-Science-Engineering-and-Technology, Bangkok: World Acad Sci, Eng & Tech-Waset.
Jørgensen, M., Indahl, U., & Sjøberg, D. (2003). Software effort estimation by analogy and ‘regression toward the mean’. Journal of Systems and Software, 68(3), 253–262. https://doi.org/10.1016/S0164-1212(03)00066-9 .
Kabir, M. M., Shahjahan, M., & Murase, K. (2011). A new local search based hybrid genetic algorithm for feature selection. Neurocomputing, 74(17), 2914–2928.
Kadoda, G., Cartwright, M., Chen, L., Shepperd, M. (2000). Experiences using case-based reasoning to predict software project effort. In EASE 2000 (pp. 2–3). Presented at the EASE 2000, Staffordshire, UK.
Keung, J., Kocaguneli, E., & Menzies, T. (2012). Finding conclusion stability for selecting the best effort predictor in software effort estimation. Automated Software Engineering, 20(4), 543–567. https://doi.org/10.1007/s10515-012-0108-5 .
Kirsopp, C., Shepperd, M. J., Hart, J. (2002). Search heuristics, case-based reasoning and software project effort prediction. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 9–13). New York, USA. http://v-scheiner.brunel.ac.uk/handle/2438/1554 . Accessed 27 Jan 2016.
Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1–2), 273–324. https://doi.org/10.1016/S0004-3702(97)00043-X .
Kwak, N., & Choi, C.-H. (2002). Input feature selection for classification problems. IEEE Transactions on Neural Networks, 13(1), 143–159. https://doi.org/10.1109/72.977291 .
Langdon, W. B., Dolado, J., Sarro, F., & Harman, M. (2016). Exact mean absolute error of baseline predictor, MARP0. Information and Software Technology, 73, 16–18. https://doi.org/10.1016/j.infsof.2016.01.003 .
Li, Y. F., Xie, M., & Goh, T. N. (2009). A study of mutual information based feature selection for case based reasoning in software cost estimation. Expert Systems with Applications, 36(3), 5921–5931.
Liu, H., & Motoda, H. (2012). Feature selection for knowledge discovery and data mining (Vol. 454). Springer Science & Business Media. https://books.google.es/books?hl=en&lr=&id=aaDbBwAAQBAJ&oi=fnd&pg=PP10&dq=Feature+selection+for+knowledge+discovery+and+data+mining&ots=iuMhcWZGcf&sig=KlmNEIcsBdDVs-m1HUuICfpYZiM . Accessed 25 Jan 2016.
Liu, H., & Yu, L. (2005). Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17(4), 491–502. https://doi.org/10.1109/TKDE.2005.66 .
Liu, H., Wei, R., & Jiang, G. (2013). A hybrid feature selection scheme for mixed attributes data. Computational and Applied Mathematics, 32(1), 145–161. https://doi.org/10.1007/s40314-013-0019-5 .
Liu, Q., Wang, J., Xiao, J., Zhu, H. (2014). Mutual information based feature selection for symbolic interval data. In International Conference on Software Intelligence Technologies and Applications International Conference on Frontiers of Internet of Things 2014 (pp. 62–69). Presented at the International Conference on Software Intelligence Technologies and Applications International Conference on Frontiers of Internet of Things 2014. https://doi.org/10.1049/cp.2014.1537 .
Lokan, C. (2005). What should you optimize when building an estimation model? In Software Metrics, 2005. 11th IEEE International Symposium (pp. 1–10). https://doi.org/10.1109/METRICS.2005.55 .
Lokan, C., & Mendes, E. (2009a). Investigating the use of chronological split for software effort estimation. Software, IET, 3(5), 422–434. https://doi.org/10.1049/iet-sen.2008.0107 .
Lokan, C., & Mendes, E. (2009b). Applying moving windows to software effort estimation. In Proceedings of the 2009 3rd international symposium on empirical software engineering and measurement (pp. 111–122). Washington, DC: IEEE Computer Society. https://doi.org/10.1109/ESEM.2009.5316019 .
Lokan, C., & Mendes, E. (2012). Investigating the use of duration-based moving windows to improve software effort prediction. In Software Engineering Conference (APSEC), 2012 19th Asia-Pacific (Vol. 1, pp. 818–827). Presented at the Software Engineering Conference (APSEC), 2012 19th Asia-Pacific. https://doi.org/10.1109/APSEC.2012.74 .
Lustgarten, J.L., Visweswaran, S., Grover, H., Gopalakrishnan, V. (2008). An evaluation of discretization methods for learning rules from biomedical datasets. In BIOCOMP (pp. 527–532).
Mandal, M., & Mukhopadhyay, A. (2013). An improved minimum redundancy maximum relevance approach for feature selection in gene expression data. Procedia Technology, 10, 20–27. https://doi.org/10.1016/j.protcy.2013.12.332 .
Mendes, E., Watson, I., Triggs, C., Mosley, N., & Counsell, S. (2003). A comparative study of cost estimation models for web hypermedia applications. Empirical Software Engineering, 8(2), 163–196.
Mendes, E., Lokan, C., Harrison, R., Triggs, C. (2005). A replicated comparison of cross-company and within-company effort estimation models using the ISBSG database. In Software Metrics, 2005. 11th IEEE International Symposium (pp. 1–10). https://doi.org/10.1109/METRICS.2005.4 .
Moses, J., Farrow, M., Parrington, N., & Smith, P. (2006). A productivity benchmarking case study using Bayesian credible intervals. Software Quality Journal, 14(1), 37–52. https://doi.org/10.1007/s11219-006-6000-4 .
Núñez, H., Sànchez-Marrè, M., Cortés, U., Comas, J., Martínez, M., Rodríguez-Roda, I., & Poch, M. (2004). A comparative study on the use of similarity measures in case-based reasoning to improve the classification of environmental system situations. Environmental Modelling & Software, 19(9), 809–819. https://doi.org/10.1016/j.envsoft.2003.03.003 .
Oh, I.-S., Lee, J.-S., & Moon, B.-R. (2004). Hybrid genetic algorithms for feature selection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 26(11), 1424–1437.
Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238. https://doi.org/10.1109/TPAMI.2005.159 .
R Core Team. (2015). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing https://www.R-project.org/ .
Romanski, P., & Kotthoff, L. (2014). FSelector: Selecting attributes. R package version 0.20. https://CRAN.R-project.org/package=FSelector .
Shannon, C. E. (1949). The mathematical theory of communication. Urbana: University of Illinois Press.
Shepperd, M., & MacDonell, S. (2012). Evaluating prediction systems in software project estimation. Information and Software Technology, 54(8), 820–827.
Shepperd, M., & Schofield, C. (1997). Estimating software project effort using analogies. Software Engineering, IEEE Transactions on, 23(11), 736–743.
Somol, P., Pudil, P., & Kittler, J. (2004). Fast branch & bound algorithms for optimal feature selection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 26(7), 900–912.
Song, Q., & Shepperd, M. (2007). A new imputation method for small software project data sets. Journal of Systems and Software, 80(1), 51–62.
Top, O. O., Ozkan, B., Nabi, M., Demirors, O. (2011). Internal and External Software Benchmark Repository Utilization for Effort Estimation. In Software Measurement, 2011 Joint Conference of the 21st Int’l Workshop on and 6th Int’l Conference on Software Process and Product Measurement (IWSM-MENSURA) (pp. 302–307). https://doi.org/10.1109/IWSM-MENSURA.2011.41 .
Vinh, L.T., Thang, N.D., Lee, Y.-K. (2010). An improved maximum relevance and minimum redundancy feature selection algorithm based on normalized mutual information. In 2010 10th IEEE/IPSJ International Symposium on Applications and the Internet (SAINT) (pp. 395–398). Presented at the 2010 10th IEEE/IPSJ International Symposium on Applications and the Internet (SAINT). https://doi.org/10.1109/SAINT.2010.50 .
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J. (2011). Data mining: Practical machine learning tools and techniques. Morgan Kaufmann.
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