@article {PALACIOS201660,
	title = {An extension of the FURIA classification algorithm to low quality data through fuzzy rankings and its application to the early diagnosis of dyslexia},
	journal = {Neurocomputing},
	volume = {176},
	year = {2016},
	note = {Recent Advancements in Hybrid Artificial Intelligence Systems and its Application to Real-World Problems},
	pages = {60 - 71},
	abstract = {An early detection and reeducation of dyslexic children is critical for their integration in the classroom. Parents and instructors can help the psychologist to detect potential cases of dyslexia before the children׳s writing age. Artificial intelligence tools can also assist in this task. Dyslexia symptoms are detected with tests whose results may be vague or ambiguous, thus machine learning techniques for low quality data are advised. In particular, in this paper it is suggested that a new extension to vague datasets of the classification algorithm FURIA (Fuzzy Unordered Rule Induction Algorithm) has advantages over other approaches in both the computational effort during the learning stage and the linguistic quality of the induced classification rules. The new approach is benchmarked with different test problems and compared to other artificial intelligence tools for dyslexia diagnosis in the literature.},
	keywords = {Dyslexia, FURIA, Fuzzy rule based classifiers, Low Quality Data},
	issn = {0925-2312},
	doi = {https://doi.org/10.1016/j.neucom.2014.11.088},
	url = {http://www.sciencedirect.com/science/article/pii/S0925231215005706},
	author = {Ana Palacios and Luciano S{\'a}nchez and In{\'e}s Couso and S{\'e}bastien Destercke}
}
@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 {PALACIOS201510,
	title = {Sequential pattern mining applied to aeroengine condition monitoring with uncertain health data},
	journal = {Engineering Applications of Artificial Intelligence},
	volume = {44},
	year = {2015},
	pages = {10 - 24},
	abstract = {Numerical algorithms that can assess Engine Health Monitoring (EHM) data in aeroengines are influenced by the high level of uncertainty inherent to gas path measurements and engine-to-engine variability. Among them, fuzzy rule-based techniques have been successfully used due to their robustness towards noisy signals and their capability to learn human-readable rules from data. These techniques are useful in detecting the presence of certain types of abnormal events or general engine deterioration, through the identification of specific combinations of EHM signals associated with these specific cases. However, there are also other types of engine events that manifest themselves as an ordered sequence of otherwise normal combinations of the EHM signals. These combinations are dismissed when considered in isolation as the current existing techniques cannot assess them. In this paper it is proposed to use sequence mining techniques in order to obtain fuzzy rules from uncertain EHM data which can in turn be used to identify the cases where an engine event is determined as a sequence of otherwise normal combinations of EHM signals. The results are subsequently tested on a representative sample of aeroengine data.},
	keywords = {engine health monitoring, Fuzzy rule-based systems, Fuzzy sequence mining, Uncertain data},
	issn = {0952-1976},
	doi = {https://doi.org/10.1016/j.engappai.2015.05.003},
	url = {http://www.sciencedirect.com/science/article/pii/S0952197615001062},
	author = {Ana Palacios and Alvaro Mart{\'\i}nez and Luciano S{\'a}nchez and In{\'e}s Couso}
}
@article {OTERO201488,
	title = {Bootstrap analysis of multiple repetitions of experiments using an interval-valued multiple comparison procedure},
	journal = {Journal of Computer and System Sciences},
	volume = {80},
	number = {1},
	year = {2014},
	pages = {88 - 100},
	abstract = {A new bootstrap test is introduced that allows for assessing the significance of the differences between stochastic algorithms in a cross-validation with repeated folds experimental setup. Intervals are used for modeling the variability of the data that can be attributed to the repetition of learning and testing stages over the same folds in cross validation. Numerical experiments are provided that support the following three claims: (1) Bootstrap tests can be more powerful than ANOVA or Friedman test for comparing multiple classifiers. (2) In the presence of outliers, interval-valued bootstrap tests achieve a better discrimination between stochastic algorithms than nonparametric tests. (3) Choosing ANOVA, Friedman or Bootstrap can produce different conclusions in experiments involving actual data from machine learning tasks.},
	keywords = {Cross validation, Statistical comparisons of algorithms, Tests for interval-valued data},
	issn = {0022-0000},
	doi = {https://doi.org/10.1016/j.jcss.2013.03.009},
	url = {http://www.sciencedirect.com/science/article/pii/S0022000013000731},
	author = {Jos{\'e} Otero and Luciano S{\'a}nchez and In{\'e}s Couso and Ana Palacios}
}
