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Applications of Machine-Learning Algorithms for Infrared Colour Selection of Galactic Wolf-Rayet Stars
We have investigated and applied machine-learning algorithms for Infrared
Colour Selection of Galactic Wolf-Rayet (WR) candidates. Objects taken from the
GLIMPSE catalogue of the infrared objects in the Galactic plane can be
classified into different stellar populations based on the colours inferred
from their broadband photometric magnitudes (, and from 2MASS, and
the four \textit{Spitzer}/IRAC bands). The algorithms tested in this pilot
study are variants of the -Nearest Neighbours (-NN) approach, which is
ideal for exploratory studies of classification problems where interrelations
between variables and classes are complicated. The aims of this study are (1)
to provide an automated tool to select reliable WR candidates and potentially
other classes of objects, (2) to measure the efficiency of infrared colour
selection at performing these tasks and, (3) to lay the groundwork for
statistically inferring the total number of WR stars in our Galaxy. We report
the performance results obtained over a set of known objects and selected
candidates for which we have carried out follow-up spectroscopic observations,
and confirm the discovery of 4 new WR stars.Comment: Authors' version of published paper, now at MNRAS, 473, 256
A review of associative classification mining
Associative classification mining is a promising approach in data mining that utilizes the
association rule discovery techniques to construct classification systems, also known as
associative classifiers. In the last few years, a number of associative classification algorithms
have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms
employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule
evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative
classification techniques with regards to the above criteria. Finally, future directions in associative
classification, such as incremental learning and mining low-quality data sets, are also
highlighted in this paper
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