Feature Selection for Data and Pattern Recognition: An Introduction - Feature Selection for Data and Pattern Recognition - page 5

of other instances, has been shown to be one of the crucial properties of time-series
data sets [ 4 , 7 ]. There are proposed some selected instances for feature construc-
tion, detailed description of the algorithms provided, and experimental results on
large number of publicly available real-world time-series data sets shown.
Chapter 12 presents an analysis of descriptors that utilize various aspects of image
data: colour, texture, gradient, and statistical moments, and this list is extended
with local features [ 2 ]. The goal of the analysis is to find descriptors that are
best suited for particular task, i.e. re-identification of objects in a multi-camera
environment. For descriptor evaluation, scatter and clustering measures [ 12 ]are
supplemented with a new measure derived from calculating direct dissimilarities
between pairs of images [ 5 , 6 ].
Chapter 13 deals with the selection of the most appropriate moment features used
to recognise known patterns [ 13 ]. For this purpose, some popular moment families
are presented and their properties are discussed. Two algorithms, a simple Genetic
Algorithm (GA) and the Relief algorithm are applied to select the moment features
that better discriminate human faces and facial expressions, under several pose and
illumination conditions [ 9 ].
Chapter 14 contains considerations on grouped features. When features are gro-
uped, it is desirable to perform feature selection groupwise in addition to selecting
individual features. It is typically the case in data obtained by modern high-
throughput genomic profiling technologies such as exon microarrays. To handle
grouped features, feature selection methods are discussed with the focus on a
popular shrinkage method, lasso, and its variants, that are based on regularized
regression with generalized linear models [ 6 ].
1.3 Concluding Remarks
In this topic some advances and research dedicated to feature selection for data and
pattern recognition are presented. Even though it has been the subject of interest for
some time, feature selection remains one of actively pursued avenues of investigations
due to its importance and bearing upon other problems and tasks. It can be studied
within a domain from which features are extracted, independently of it, taking into
account specific properties of involved algorithms and techniques, with feedback
from applications, or without it. Observations from executed experiments can bring
local and global conclusions, with theoretical and practical significance.
References
1. Abraham, A., Falcón, R., Bello, R. (eds.): Rough Set Theory: A True Landmark in Data
Analysis. Studies in Computational Intelligence, vol. 174. Springer, Berlin (2009)
2. Baxes, G.A.: Digital Image Processing: Principles and Applications. Wiley, New York (1994)