Mikel ElkanoUniversidad Pública de Navarra | UPNA · Department of Statistics, Computer Science, and Mathematics
Mikel Elkano
CTO at Neuraptic AI
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Publications (22)
The definition of linguistic terms is a critical part of the construction of any fuzzy classifier. Fuzzy partitioning methods (FPMs) range from simple uniform partitioning to sophisticated optimization algorithms. In this paper we present FUZZ-EQ, a preprocessing algorithm that facilitates the construction of meaningful fuzzy partitions regardless...
The Era of Big Data has forced researchers to explore new distributed solutions for building fuzzy classifiers, which often introduce approximation errors or make strong assumptions to reduce computational and memory requirements. As a result, Big Data classifiers might be expected to be inferior to those designed for standard classification tasks...
In this work, we discuss a recent generalization of the classical notion of monotonicity, with a special focus on the idea of directional monotonicity. This idea leads to the concepts of pre-aggregation functions and of ordered directional monotonicity. For the former, the direction along which monotonicity is considered is the same for all the poi...
We present a new distributed fuzzy partitioning method to reduce the complexity of multi-way fuzzy decision trees in Big Data classification problems. The proposed algorithm builds a fixed number of fuzzy sets for all variables and adjusts their shape and position to the real distribution of training data. A two-step process is applied : 1) transfo...
The growing amount of available data has become a serious challenge to data mining and machine learning techniques. Well-known classification methods that have been widely applied so far are no longer feasible in Big Data environments. For this reason, prototype reduction techniques (both selection and generation) come up as a candidate solution to...
The scarcity of labeled data has limited the capacity of convolutional neural networks (CNNs) until not long ago and still represents a serious problem in a number of image processing applications. Unsupervised methods have been shown to perform well in feature extraction and clustering tasks, but further investigation on unsupervised solutions for...
The previous Fuzzy Rule-Based Classification Systems (FRBCSs) for Big Data problems consist in concurrently learning multiple Chi et al. FRBCSs whose rule bases are then aggregated. The problem of this approach is that different models are obtained when varying the configuration of the cluster, becoming less accurate as more computing nodes are add...
The aim of this paper is to propose a consensus method via penalty functions for decision making in ensembles of fuzzy rule-based classification systems (FRBCSs). For that, we first introduce a method based on overlap indices for building confidence and support measures, which are usually used to evaluate the degree of certainty or interest of a ce...
Decomposition strategies have been shown to be a successful methodology to tackle multi-class classification problems. Among them, One-vs-One approach is a commonly used technique that consists in dividing the original multi-class problem into easier-to-solve binary sub-problems considering each possible pair of classes. However, this methodology i...
In this paper we propose an algorithm to solve group decision making problems using n-dimensional fuzzy sets, namely, sets in which the membership degree of each element to the set is given by an increasing tuple of n elements. The use of these sets has naturally led us to define admissible orders for n-dimensional fuzzy sets, to present a construc...
This paper introduces the concept of Choquet-like Copula-based aggregation function (CC-integral) and its application in fuzzy rule-based classification systems. The standard Choquet integral is expanded by distributing the product operation. Then, the product operation is generalized by a copula. Unlike the generalization of the Choquet integral b...
In this paper we present the composition of interval-valued fuzzy relations using interval-valued aggregation functions. In particular, we propose a generalization of Zadeh’s composition rule, replacing the minimum by an interval-valued aggregation function. We analyze the preservation of different properties of interval-valued fuzzy relations by t...
In this work we present an optimization of the only Fuzzy Rule-Based Classification System that is able to face Big Data classification problems to date, i.e., Chi-FRBCS-BigDataCS. The aim of this optimization is to speed up the learning process of the algorithm without affecting the model obtained. Our proposal is based on the usage of Look-Up-Tab...
In this work we introduce a new class of OWA operators for Atanassov intuitionistic fuzzy sets which distinguishes between the weights for the membership degree and the weights for the nonmembership degree; we call these operators Unbalanced Atanassov Intuitionistic OWA operators. We also study under which conditions these operators are aggregation...
Classification problems with multiple classes suppose a challenge in Data Mining tasks. There is a difficulty inherent to the learning process when trying to find the most adequate discrimination functions among the different concepts within the dataset. Using Fuzzy Rule Based Classification Systems in general, and Evolutionary Fuzzy Systems in par...
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 (Fuz...
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 behavi...
In a previous work we proposed to enhance the performance of FARC-HD fuzzy classifier in multi-class classification problems using decomposition strategies. This synergy was further improved by introducing n-dimensional overlap functions in the learning algorithm and the inference of FARC-HD instead of the product t-norm.
In this work, we extend t...
In this work we study the behavior of the FARC-HD method when addressing multi-class classification problems using the One-vs-One (OVO) decomposition strategy. We will show that the confidences provided by FARC-HD (due to the use of the product in the inference process) are not suitable for this strategy. This problem implies that robust strategies...