BUSCADOR RECOLECTA

In the analysis of relations among the elements of two sets it is usual to obtain different values depending on the point of view from which these relations are measured. The main goal of the paper is the modelization of these situations by means of a generalization of the L-fuzzy concept analysis called L-fuzzy bicontext. We study the L-fuzzy concepts of these L-fuzzy bicontexts obtaining some interesting results. Specifically, we will be able to classify the biconcepts of the L-fuzzy bicontext. Finally, a practical case is developed using this new tool., This work has been partially supported by the Research Group “Intelligent
Systems and Energy (SI+E)” of the Basque Government, under
Grant IT677-13, by the Research Groups “Artificial Intelligence and Approximate
Reasoning” and “Adquisición de conocimiento y minería de datos,
funciones especiales y métodos numéricos avanzados” of the Public University
of Navarra and by project TIN2013-40765-P.

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented., This work was supported by the Research Projects CAB(CDTI),
TIN2011-28488, and TIN2013-40765-P.D

In this work we introduce the notion of preaggregation
function. Such a function satisfies the same boundary
conditions as an aggregation function, but, instead of requiring
monotonicity, only monotonicity along some fixed direction (directional
monotonicity) is required. We present some examples
of such functions. We propose three different methods to build
pre-aggregation functions. We experimentally show that in fuzzy
rule-based classification systems, when we use one of these
methods, namely, the one based on the use of the Choquet
integral replacing the product by other aggregation functions,
if we consider the minimum or the Hamacher product t-norms
for such construction, we improve the results obtained when
applying the fuzzy reasoning methods obtained using two classical
averaging operators like the maximum and the Choquet integral., This work was supported in part by the Spanish Ministry of Science
and Technology under projects TIN2008-06681-C06-01, TIN2010-
15055, TIN2013-40765-P, TIN2011-29520.

Fingerprint recognition has found a reliable application for verification or identification of people in biometrics. Globally, fingerprints can be viewed as valuable traits due to several perceptions observed by the experts; such as the distinctiveness and the permanence on humans and the performance in real applications. Among the main stages of fingerprint recognition, the automated matching phase has received much attention from the early years up to nowadays. This paper is devoted to review and categorize the vast number of fingerprint matching methods proposed in the specialized literature. In particular, we focus on local minutiae-based matching algorithms, which provide good performance with an excellent trade-off between efficacy and efficiency. We identify the main properties and differences of existing methods. Then, we include an experimental evaluation involving the most representative local minutiae-based matching models in both verification and evaluation tasks. The results obtained will be discussed in detail, supporting the description of future directions., This work was supported by the Research Projects CAB (CDTI), TIN2011-28488, and TIN2013-40765-P. D.

In this position paper we propose a consistent and unifying view to all those basic knowledge representation models that are based on the existence of two somehow opposite fuzzy concepts. A number of these basic models can be found in fuzzy logic and multi-valued logic literature. Here it is claimed that it is the semantic relationship between two paired concepts what determines the emergence of different types of neutrality, namely indeterminacy, ambivalence and conflict, widely used under different frameworks (possibly under different names). It will be shown the potential relevance of paired structures, generated from two paired concepts together with their associated neutrality, all of them to be modeled as fuzzy sets. In this way, paired structures can be viewed as a standard basic model from which different models arise. This unifying view should therefore allow a deeper analysis of the relationships between several existing knowledge representation formalisms, providing a basis from which more expressive models can be later developed., This research has been partially supported by the Government of Spain (grants TIN2015-66471-P and TIN2013-40765-P), the Government of Madrid (grant S2013/ICCE-2845) and the UCM (Research Group 910149).

In this work, we consider a complete lattice L and we study L-fuzzy context sequences which
represent the evolution in time of an L-fuzzy context. To carry out this study, in the first part of
the paper, we consider n-ary OWA operators in complete lattices, which enable us to make a
general analysis and a temporal analysis at any moment in time of L-fuzzy context sequences.
After that, evolution in time of the relationship between the objects and the attributes is
considered. In particular, we analyze the concepts of Trend and Persistent formal contexts.
Finally, we illustrate our results with an example where we consider the particular lattice
L = J ([0, 1])., This work has been partially supported by the Research Group “Intelligent Systems and Energy (SI+E)” of the Basque Government,
under Grant IT677-13, and by the Research Group “Artificial Intelligence and Approximate Reasoning” of the Public
University of Navarra, of the Spanish Government, under Project TIN2013-40765-P.

In this work we introduce a method for constructing linear orders between pairs of intervals by using
aggregation functions. We adapt this method to the case of interval-valued Atanassov intuitionistic fuzzy
sets and we apply these sets and the considered orders to a decision making problem., The work has been supported by projects TIN2013-40765-P and
MTM2012-37894-C02-02 of the Spanish Ministry of Science and
the Research Services of the Universidad Publica de Navarra.

In this paper, we will present a wider view on the
relationship between interval-valued fuzzy sets and interval type-
2 fuzzy sets where we will show that interval-valued fuzzy sets
are a particular case of the interval type-2 fuzzy sets. For this
reason, both concepts should be treated in a different way. In
addition, the view presented in this paper will allow a more
general perspective of interval type-2 fuzzy sets which will allow
representing concepts which could not be presented by intervalvalued
fuzzy sets., This work was supported in part by the Spanish Ministry of Science and Technology under Project TIN2011-28488 and Project TIN2013-40765-P.

The existence of a continuous and order-preserving real-valued function, for the class of continuous and ordinal invariant total preorders, defined on the Banach space of all bounded real-valued functions, which are in turn defined on a given set Ω, is characterized. Whenever the total preorder is nontrivial, the type of representation obtained leads to a functional equation that is closely related to the concept of comparison meaningfulness, and is studied in detail in this setting. In particular, when restricted to the space of bounded and measurable real-valued functions, with respect to some algebra of subsets of Ω, we prove that, if the total preorder is also weakly Paretian, then it can be represented as a Choquet integral with respect to a {0,1}-valued capacity. Some interdisciplinary applications to measurement theory and social choice are also considered., This work has been partially supported by the research projects ECO2012-34828, MTM2012-37894-C02-02 and TIN2013-40765-P (Spain).

In the first part of this paper we reviewed the fingerprint classification literature from two different perspectives: the feature extraction and the classifier learning. Aiming at answering the question of which among the reviewed methods would perform better in a real implementation we end up in a discussion which showed the difficulty in answering this question. No previous comparison exists in the literature and comparisons among papers are done with different experimental frameworks. Moreover, the difficulty in implementing published methods was stated due to the lack of details in their description, parameters and the fact that no source code is shared. For this reason, in this paper we will go through a deep experimental study following the proposed double perspective. In order to do so, we have carefully implemented some of the most relevant feature extraction methods according to the explanations found in the corresponding papers and we have tested their performance with different classifiers, including those specific proposals made by the authors. Our aim is to develop an objective experimental study in a common framework, which has not been done before and which can serve as a baseline for future works on the topic. This way, we will not only test their quality, but their reusability by other researchers and will be able to indicate which proposals could be considered for future developments. Furthermore, we will show that combining different feature extraction models in an ensemble can lead to a superior performance, significantly increasing the results obtained by individual models., This work was supported by the Research Projects CAB(CDTI),
TIN2011-28488, and TIN2013-40765-P.

The final goal of this study is to adapt the concept
of fuzzy entropy of De Luca and Termini to deal with Type-2
Fuzzy Sets. We denote this concept Type-2 Fuzzy Entropy-Set.
However, the construction of the notion of entropy measure on
an infinite set, such us [0, 1], is not effortless. For this reason,
we first introduce the concept of quasi-entropy of a Fuzzy Set
on the universe [0, 1]. Furthermore, whenever the membership
function of the considered Fuzzy Set in the universe [0, 1] is
continuous, we prove that the quasi-entropy of that set is a fuzzy
entropy in the sense of De Luca y Termini. Finally, we present
an illustrative example where we use Type-2 Fuzzy Entropy-Sets
instead of fuzzy entropies in a classical fuzzy algorithm., This work was supported by the Research Service of Universidad Publica
de Navarra as well as by the projects TIN2013-40765-P and TIN2012-32482 of
the Spanish Ministry of Science and by the Brazilian funding agencies CNPq
(under Proc. No. 480832/2011-0 and No. 307681/2012-2) and CAPES (under
Proc. No. 5778/2014-00).

In this paper we will present two theorems for the
join and meet operations for general type-2 fuzzy sets with
arbitrary secondary memberships, which can be non-convex
and/or non-normal type-1 fuzzy sets. These results will be used
to derive the join and meet operations of the more general
descriptions of interval type-2 fuzzy sets presented in [1], where
the secondary grades can be non-convex. Hence, this work will
help to explore the potential of type-2 fuzzy logic systems which
use the general forms of interval type-2 fuzzy sets which are
not equivalent to interval valued fuzzy sets. Several examples
for both general type-2 and the more general forms of interval
type-2 fuzzy sets are presented., This work was supported in part by the Spanish Ministry of Science and Technology under project TIN2013-40765-P

This work tackles the comparison of radial data, and proposes comparison measures that are further applied to fingerprint analysis. First, we study the similarity of scalar and non-scalar radial data, elaborated on previous works in fuzzy set theory. This study leads to the concepts of restricted radial equivalence function and Radial Similarity Measure, which model the perceived similarity between scalar and vectorial pieces of radial data, respectively. Second, the utility of these functions is tested in the context of fingerprint analysis, and more specifically, in the singular point detection. With this aim, a novel Template-based Singular Point Detection method is proposed, which takes advantage of these functions. Finally, their suitability is tested in different fingerprint databases. Different Similarity Measures are considered to show the flexibility offered by these measures and the behaviour of the new method is compared with well-known singular point detection methods., The authors gratefully acknowledge the financial support of the Spanish Ministry of Science (project TIN2013-
40765-P), the Research Services of Universidad Pública de Navarra, as well as the financial support of the Research
Foundation Flanders (FWO project 3G.0838.12.N).

This is an accepted manuscript of an article published by Taylor & Francis in International Journal of Computational Intelligence Systems, Vol. 7, No. 5, 993-1001 in October 2014, available online: http://dx.doi.org/10.1080/18756891.2014.967005, In this work, using the identification between implication operators and aggregation functions, we study
the implication operators that are recovered from overlap functions. In particular, we focus in which
properties of implication operators are preserved. We also study how negations can be defined in terms of
overlap functions., This research has been partially supported by Grant TIN2013-40765-P from the Government of Spain and the Research Services of the Universidad Publica
de Navarra.

In this work we review the definition and basic
properties of the different types of fuzzy sets that have appeared
up to now in the literature. We also analyze the relationships
between them and enumerate some of the applications in which
they have been used., This work was supported by projects TIN2013-40765-P and TIN2012-32482 of the Spanish Ministry of Science and by the Brazilian funding agency CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnologico), under Proc. Nos. 480832/2011-0 and 307681/2012-2

Based on the definition of admissible order for
interval-valued Atanassov intuitionistic fuzzy sets, we study
OWA operators in these sets distinguishing between the weights
associated to the membership and those associated to the nonmembership
degree which may differ from the latter. We also study Choquet integrals for aggregating information which is represented using interval-valued Atanassov intuitionistic fuzzy sets. We conclude with two algorithms to choose the best
alternative in a decision making problem when we use this kind
of sets to represent information., This work has been partially supported by the project
TIN2013-40765-P (Spain).

Multi-class classification problems appear in a broad variety of real-world problems, e.g., medicine, genomics, bioinformatics, or computer vision. In this context, decomposition strategies are useful to increase the classification performance of classifiers. For this reason, in a previous work we proposed to improve the performance of FARC-HD (Fuzzy Association Rule-based Classification model for High-Dimensional problems) fuzzy classifier using One-vs-One (OVO) and One-vs-All (OVA) decomposition strategies. As a result of an exhaustive experimental analysis, we concluded that even though the usage of decomposition strategies was worth to be considered, further improvements could be achieved by introducing n-dimensional overlap functions instead of the product t-norm in the Fuzzy Reasoning Method (FRM). In this way, we can improve confidences for the subsequent processing performed in both OVO and OVA.

In this paper, we want to conduct a broader study of the influence of the usage of n-dimensional overlap functions to model the conjunction in several Fuzzy Rule-Based Classification Systems (FRBCSs) in order to enhance their performance in multi-class classification problems applying decomposition techniques. To do so, we adapt the FRM of four well-known FRBCSs (CHI, SLAVE, FURIA, and FARC-HD itself). We will show that the benefits of the usage of n-dimensional overlap functions strongly depend on both the learning algorithm and the rule structure of each classifier, which explains why FARC-HD is the most suitable one for the usage of these functions., This work has been supported by the Spanish Ministry of Science and Technology under the project
TIN-2013-40765-P.

In this chapter, we show how the concepts of overlap function and overlap index can be used to define fuzzy measures which depend on the specific data of each considered problem., The work has been supported by projects TIN2013-40765-P and TIN2015-66471-P of the Spanish Ministry of Science, by grant VEGA 1/0420/15 and by grant VEGA 1/0419/13.

In classification, noise may deteriorate the system performance and increase the complexity of the models built. In order to mitigate its consequences, several approaches have been proposed in the literature. Among them, noise filtering, which removes noisy examples from the training data, is one of the most used techniques. This paper proposes a new noise filtering method that combines several filtering strategies in order to increase the accuracy of the classification algorithms used after the filtering process. The filtering is based on the fusion of the predictions of several classifiers used to detect the presence of noise. We translate the idea behind multiple classifier systems, where the information gathered from different models is combined, to noise filtering. In this way, we consider the combination of classifiers instead of using only one to detect noise. Additionally, the proposed method follows an iterative noise filtering scheme that allows us to avoid the usage of detected noisy examples in each new iteration of the filtering process. Finally, we introduce a noisy score to control the filtering sensitivity, in such a way that the amount of noisy examples removed in each iteration can be adapted to the necessities of the practitioner. The first two strategies (use of multiple classifiers and iterative filtering) are used to improve the filtering accuracy, whereas the last one (the noisy score) controls the level of conservation of the filter removing potentially noisy examples. The validity of the proposed method is studied in an exhaustive experimental study. We compare the new filtering method against several state-of-the-art methods to deal with datasets with class noise and study their efficacy in three classifiers with different sensitivity to noise., Acknowledgment supported by the projects TIN2011-28488, TIN2013-40765-P, P10-TIC-06858 and P11-TIC- 7765. J. A. Sáez was supported by EC under FP7, Coordination and Support Action, Grant Agreement Number 316097, ENGINE European Research Centre of Network Intelligence for Innovation Enhancement (http://engine.pwr.wroc.pl/).

There are many real-world classification problems involving multiple classes, e.g., in bioinformatics, computer vision or medicine. These problems are generally more difficult than their binary counterparts. In this scenario, decomposition strategies usually improve the performance of classifiers. Hence, in this paper we aim to improve the behaviour of FARC-HD fuzzy classifier in multi-class classification problems using decomposition strategies, and more specifically One-vs-One (OVO) and One-vs-All (OVA) strategies. However, when these strategies are applied on FARC-HD a problem emerges due to the low confidence values provided by the fuzzy reasoning method. This undesirable condition comes from the application of the product t-norm when computing the matching and association degrees, obtaining low values, which are also dependent on the number of antecedents of the fuzzy rules. As a result, robust aggregation strategies in OVO such as the weighted voting obtain poor results with this fuzzy classifier. In order to solve these problems, we propose to adapt the inference system of FARC-HD replacing the product t-norm with overlap functions. To do so, we define n-dimensional overlap functions. The usage of these new functions allows one to obtain more adequate outputs from the base classifiers for the subsequent aggregation in OVO and OVA schemes. Furthermore, we propose a new aggregation strategy for OVO to deal with the problem of the weighted voting derived from the inappropriate confidences provided by FARC-HD for this aggregation method. The quality of our new approach is analyzed using twenty datasets and the conclusions are supported by a proper statistical analysis. In order to check the usefulness of our proposal, we carry out a comparison against some of the state-of-the-art fuzzy classifiers. Experimental results show the competitiveness of our method., This work was supported in part by the Spanish Ministry of Science and
Technology under projects TIN2011-28488, TIN-2012-33856 and TIN-2013-
40765-P and the Andalusian Research Plan P10-TIC-6858 and P11-TIC-7765.

The final publication is available at Springer via http://dx.doi.org/ 10.1007/s13748-016-0086-0, In this work, we introduce a new class of functions defned on the
interval-valued setting. These functions extend classical OWA operators but
allow for diferent weighting vectors to handle the lower bounds and the upper
bounds of the considered intervals. As a consequence, the resulting functions
need not be an interval-valued aggregation function, so we study, in the case of
the lexicographical order, when these operators give an interval as output and
are monotone. We also discuss an illustrative example on a decision making
problem in order to show the usefulness of our developments., Authors were supported by Project TIN2013-40765-P of the Spanish Government.

The current financial crisis has stressed the need of obtaining more accurate prediction models in order to decrease the risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue. Furthermore, there is a need to handle the real-world imbalanced financial data sets without using sampling techniques which might introduce noise in the used data. In this paper, we present a compact evolutionary interval-valued fuzzy rule-based classification system, which is based on IVTURSFARC-HD (Interval-Valued fuzzy rule-based classification system with TUning and Rule Selection) [22]), for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good predictions accuracies using a small set of short fuzzy rules implying a high degree of interpretability of the generated linguistic model. Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and thus avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including eleven real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1 and interval-valued fuzzy counterparts which use the SMOTE sampling technique to preprocess data and the original FURIA, which is a fuzzy approximative classifier. Furthermore, the proposed method enhances the results achieved by the cost sensitive C4.5 and it gives competitive results when compared with FURIA using SMOTE, while our proposal avoids pre-processing techniques and it provides interpretable models that allow obtaining more accurate results., This work was supported in part by the Spanish Ministry of Science and Technology under Project TIN2011-28488 and Project TIN2013-40765.