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Application of mutual information-based sequential feature selection to ISBSG mixed data

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Application of mutual information-based sequential feature selection to ISBSG mixed data

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dc.contributor.author Fernández-Diego, Marta es_ES
dc.contributor.author González-Ladrón-de-Guevara, Fernando es_ES
dc.date.accessioned 2019-05-31T20:44:39Z
dc.date.available 2019-05-31T20:44:39Z
dc.date.issued 2018 es_ES
dc.identifier.issn 0963-9314 es_ES
dc.identifier.uri http://hdl.handle.net/10251/121378
dc.description.abstract [EN] There is still little research work focused on feature selection (FS) techniques including both categorical and continuous features in Software Development Effort Estimation (SDEE) literature. This paper addresses the problem of selecting the most relevant features from ISBSG (International Software Benchmarking Standards Group) dataset to be used in SDEE. The aim is to show the usefulness of splitting the ranked list of features provided by a mutual information-based sequential FS approach in two, regarding categorical and continuous features. These lists are later recombined according to the accuracy of a case-based reasoning model. Thus, four FS algorithms are compared using a complete dataset with 621 projects and 12 features from ISBSG. On the one hand, two algorithms just consider the relevance, while the remaining two follow the criterion of maximizing relevance and also minimizing redundancy between any independent feature and the already selected features. On the other hand, the algorithms that do not discriminate between continuous and categorical features consider just one list, whereas those that differentiate them use two lists that are later combined. As a result, the algorithms that use two lists present better performance than those algorithms that use one list. Thus, it is meaningful to consider two different lists of features so that the categorical features may be selected more frequently. We also suggest promoting the usage of Application Group, Project Elapsed Time, and First Data Base System features with preference over the more frequently used Development Type, Language Type, and Development Platform. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Software Quality Journal es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Feature selection es_ES
dc.subject Mutual information es_ES
dc.subject ISBSG es_ES
dc.subject Software development effort estimation es_ES
dc.subject K-nearest neighbor es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.title Application of mutual information-based sequential feature selection to ISBSG mixed data es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11219-017-9391-5 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses es_ES
dc.description.bibliographicCitation Fernández-Diego, M.; González-Ladrón-De-Guevara, F. (2018). Application of mutual information-based sequential feature selection to ISBSG mixed data. Software Quality Journal. 26(4):1299-1325. https://doi.org/10.1007/s11219-017-9391-5 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.1007/s11219-017-9391-5 es_ES
dc.description.upvformatpinicio 1299 es_ES
dc.description.upvformatpfin 1325 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 26 es_ES
dc.description.issue 4 es_ES
dc.relation.pasarela S\374638 es_ES
dc.description.references 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 . es_ES
dc.description.references 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. es_ES
dc.description.references 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 . es_ES
dc.description.references Battiti, R. (1994). Using mutual information for selecting features in supervised neural net learning. Neural Networks, IEEE Transactions, 5(4), 537–550. es_ES
dc.description.references 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 . es_ES
dc.description.references 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 . es_ES
dc.description.references Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16–28. es_ES
dc.description.references 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 . es_ES
dc.description.references 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 . es_ES
dc.description.references 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. es_ES
dc.description.references Dash, M., & Liu, H. (2003). Consistency-based search in feature selection. Artificial Intelligence, 151(1), 155–176. es_ES
dc.description.references 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 . es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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 . es_ES
dc.description.references 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 . es_ES
dc.description.references 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. es_ES
dc.description.references 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 . es_ES
dc.description.references 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. es_ES
dc.description.references Fleuret, F. (2004). Fast binary feature selection with conditional mutual information. Journal of Machine Learning Research, 5, 1531–1555. es_ES
dc.description.references 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 . es_ES
dc.description.references 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. es_ES
dc.description.references Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. The Journal of Machine Learning Research, 3, 1157–1182. es_ES
dc.description.references 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 . es_ES
dc.description.references 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. es_ES
dc.description.references Hill, P. (2010). Practical software project estimation: a toolkit for estimating software development effort & duration. McGraw Hill Professional. es_ES
dc.description.references 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. es_ES
dc.description.references 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 . es_ES
dc.description.references 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 . es_ES
dc.description.references 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 . es_ES
dc.description.references ISBSG. (2013a). ISBSG Dataset Release 12. ISBSG. http://isbsg.org/ . Accessed 1 Mar 2016. es_ES
dc.description.references ISBSG. (2013b). ISBSG Guidelines Release 12. es_ES
dc.description.references ISBSG. (2013c). ISBSG Data Demographics Release 12. es_ES
dc.description.references 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 . es_ES
dc.description.references 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. es_ES
dc.description.references 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 . es_ES
dc.description.references Kabir, M. M., Shahjahan, M., & Murase, K. (2011). A new local search based hybrid genetic algorithm for feature selection. Neurocomputing, 74(17), 2914–2928. es_ES
dc.description.references 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. es_ES
dc.description.references 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 . es_ES
dc.description.references 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. es_ES
dc.description.references 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 . es_ES
dc.description.references 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 . es_ES
dc.description.references 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 . es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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 . es_ES
dc.description.references 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 . es_ES
dc.description.references 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 . es_ES
dc.description.references 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 . es_ES
dc.description.references 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 . es_ES
dc.description.references 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 . es_ES
dc.description.references 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 . es_ES
dc.description.references 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). es_ES
dc.description.references 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 . es_ES
dc.description.references 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. es_ES
dc.description.references 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 . es_ES
dc.description.references 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 . es_ES
dc.description.references 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 . es_ES
dc.description.references 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. es_ES
dc.description.references 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 . es_ES
dc.description.references R Core Team. (2015). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing https://www.R-project.org/ . es_ES
dc.description.references Romanski, P., & Kotthoff, L. (2014). FSelector: Selecting attributes. R package version 0.20. https://CRAN.R-project.org/package=FSelector . es_ES
dc.description.references Shannon, C. E. (1949). The mathematical theory of communication. Urbana: University of Illinois Press. es_ES
dc.description.references Shepperd, M., & MacDonell, S. (2012). Evaluating prediction systems in software project estimation. Information and Software Technology, 54(8), 820–827. es_ES
dc.description.references Shepperd, M., & Schofield, C. (1997). Estimating software project effort using analogies. Software Engineering, IEEE Transactions on, 23(11), 736–743. es_ES
dc.description.references 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. es_ES
dc.description.references Song, Q., & Shepperd, M. (2007). A new imputation method for small software project data sets. Journal of Systems and Software, 80(1), 51–62. es_ES
dc.description.references 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 . es_ES
dc.description.references 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 . es_ES
dc.description.references Witten, I.H., Frank, E., Hall, M.A., Pal, C.J. (2011). Data mining: Practical machine learning tools and techniques. Morgan Kaufmann. es_ES


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