Abstract:
In the feature subset selection problem, a
learning algorithm is faced with the problem of
selecting a relevant subset of features upon which to
focus its attention to achieve the highest predictive
accuracy with the learning algorithm on this
domain, a feature subset selection method should
consider how the algorithm and the training data
interact with filter method. This paper applies the
normalization by decimal scaling process before
feature selection to speed up the learning phase and
prevent attributes with initially smaller ranges. This
paper uses sequential forward selection to improve
the generalization performance of pattern
recognizers for water pollute or not. k-Nearest
Neighbor classifier is built with filter approach by
using the sequential forward selection. To estimate
how accurately a classifier labels future data, this
paper evaluates the performance of k-Nearest
Neighbor classifier on the complete features and the
selected feature subset by using the k-fold crossvalidation.
