@article {OTERO201642,
	title = {Finding informative code metrics under uncertainty for predicting the pass rate of online courses},
	journal = {Information Sciences},
	volume = {373},
	year = {2016},
	pages = {42 - 56},
	abstract = {A method is proposed for predicting the pass rate of a Computer Science course. Input data comprises different software metrics that are evaluated on a set of programs, comprising students{\textquoteright} answers to a list of computing challenges proposed by the course instructor. Different kinds of uncertainty are accepted, including missing answers and multiple responses to the same challenge. The most informative metrics are selected according to an extension to vague data of the observed Fisher information. The proposed method was tested on experimental data collected during two years at Oviedo University. Yearly changes in the pass rate of two groups were accurately predicted on the basis of 7 software metrics. 73 volunteer students and 1500 source files were involved in the experimentation.},
	keywords = {Automatic grading, feature selection, Genetic Fuzzy Systems, Low Quality Data, vague data},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2016.08.090},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025516306715},
	author = {Jos{\'e} Otero and Luis Junco and Rosario Su{\'a}rez and Ana Palacios and In{\'e}s Couso and Luciano S{\'a}nchez}
}
@article {SANCHEZ2008607,
	title = {Mutual information-based feature selection and partition design in fuzzy rule-based classifiers from vague data},
	journal = {International Journal of Approximate Reasoning},
	volume = {49},
	number = {3},
	year = {2008},
	pages = {607 - 622},
	abstract = {Algorithms for preprocessing databases with incomplete and imprecise data are seldom studied. For the most part, we lack numerical tools to quantify the mutual information between fuzzy random variables. Therefore, these algorithms (discretization, instance selection, feature selection, etc.) have to use crisp estimations of the interdependency between continuous variables, whose application to vague datasets is arguable. In particular, when we select features for being used in fuzzy rule-based classifiers, we often use a mutual information-based ranking of the relevance of inputs. But, either with crisp or fuzzy data, fuzzy rule-based systems route the input through a fuzzification interface. The fuzzification process may alter this ranking, as the partition of the input data does not need to be optimal. In our opinion, to discover the most important variables for a fuzzy rule-based system, we want to compute the mutual information between the fuzzified variables, and we should not assume that the ranking between the crisp variables is the best one. In this paper we address these problems, and propose an extended definition of the mutual information between two fuzzified continuous variables. We also introduce a numerical algorithm for estimating the mutual information from a sample of vague data. We will show that this estimation can be included in a feature selection algorithm, and also that, in combination with a genetic optimization, the same definition can be used to obtain the most informative fuzzy partition for the data. Both applications will be exemplified with the help of some benchmark problems.},
	keywords = {feature selection, Fuzzy fitness, Genetic Fuzzy Systems, vague data},
	issn = {0888-613X},
	doi = {https://doi.org/10.1016/j.ijar.2008.06.005},
	url = {http://www.sciencedirect.com/science/article/pii/S0888613X08001102},
	author = {Luciano S{\'a}nchez and M. Rosario Su{\'a}rez and J.R. Villar and In{\'e}s Couso}
}
