Enhancing multi-class classification in FARC-HD fuzzy classifier: On the synergy between n-dimensional overlap functions and decomposition strategies
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Show full item recordEditorial
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Materia
Multi-classification One-vs-one Linguistic Fuzzy Rule-Based Classification Systems Aggregation Overlaps
Date
2015Referencia bibliográfica
M. Elkano et al., "Enhancing Multiclass Classification in FARC-HD Fuzzy Classifier: On the Synergy Between $n$-Dimensional Overlap Functions and Decomposition Strategies," in IEEE Transactions on Fuzzy Systems, vol. 23, no. 5, pp. 1562-1580, Oct. 2015, [doi: 10.1109/TFUZZ.2014.2370677]
Abstract
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.