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A niching memetic algorithm for simultaneous clustering and feature selection
Clustering is inherently a difficult task, and is made even more difficult when the selection of relevant features is also an issue. In this paper we propose an approach for simultaneous clustering and feature selection using a niching memetic algorithm. Our approach (which we call NMA_CFS) makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solutions in both clustering and feature selection, without making any a priori assumption about the number of clusters. Within the NMA_CFS procedure, a variable composite representation is devised to encode both feature selection and cluster centers with different numbers of clusters. Further, local search operations are introduced to refine feature selection and cluster centers encoded in the chromosomes. Finally, a niching method is integrated to preserve the population diversity and prevent premature convergence. In an experimental evaluation we demonstrate the effectiveness of the proposed approach and compare it with other related approaches, using both synthetic and real data
Optimal Clustering Framework for Hyperspectral Band Selection
Band selection, by choosing a set of representative bands in hyperspectral
image (HSI), is an effective method to reduce the redundant information without
compromising the original contents. Recently, various unsupervised band
selection methods have been proposed, but most of them are based on
approximation algorithms which can only obtain suboptimal solutions toward a
specific objective function. This paper focuses on clustering-based band
selection, and proposes a new framework to solve the above dilemma, claiming
the following contributions: 1) An optimal clustering framework (OCF), which
can obtain the optimal clustering result for a particular form of objective
function under a reasonable constraint. 2) A rank on clusters strategy (RCS),
which provides an effective criterion to select bands on existing clustering
structure. 3) An automatic method to determine the number of the required
bands, which can better evaluate the distinctive information produced by
certain number of bands. In experiments, the proposed algorithm is compared to
some state-of-the-art competitors. According to the experimental results, the
proposed algorithm is robust and significantly outperform the other methods on
various data sets
Self-Configuring and Evolving Fuzzy Image Thresholding
Every segmentation algorithm has parameters that need to be adjusted in order
to achieve good results. Evolving fuzzy systems for adjustment of segmentation
parameters have been proposed recently (Evolving fuzzy image segmentation --
EFIS [1]. However, similar to any other algorithm, EFIS too suffers from a few
limitations when used in practice. As a major drawback, EFIS depends on
detection of the object of interest for feature calculation, a task that is
highly application-dependent. In this paper, a new version of EFIS is proposed
to overcome these limitations. The new EFIS, called self-configuring EFIS
(SC-EFIS), uses available training data to auto-configure the parameters that
are fixed in EFIS. As well, the proposed SC-EFIS relies on a feature selection
process that does not require the detection of a region of interest (ROI).Comment: To appear in proceedings of The 14th International Conference on
Machine Learning and Applications (IEEE ICMLA 2015), Miami, Florida, USA,
201
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