Abstract:
Classification is a process of finding the
common properties among various features and
classifying them into classes.The feature selection
process is very important which selects a subset of
relevant features, so that the selected subset is
sufficient to perform the classification
task.Classification of the real world problem can
be made easier and less time consuming by
removing the irrelevant features.In this paper, an
efficient feature selection method based on
information gainis presented for finding and
selecting relevant features which maximized the
accuracy of classifier.Features that are relevant
for rule-based classifier are selected by using
information gain.Information gain calculates the
gain value of each feature in the dataset to select
the best(relevant) feature subset. Then, rule-based
classifier generates the rules by using
selected(best) feature subset to classify the class
label of an unknown sample.The feature selection
based on information gain yields the significant
improvement of accuracy by using crossvalidation
for classification of fish dataset from
fish profile.com repository when using rule-based
classifier.
