Abstract:
Background: Feature selection is one of the most important parts of machine learning for
predicting the outcome. There are methods for selecting features or generating feature
subset. Such as: Filter method, wrapper method. Previously features were selected using
any one of these two methods. The result from the method was pretty good but it could be
better.
Objective: The objective of my thesis is to get the more accurate result. Here I am
emphasizing on feature selection for getting the more accurate results. There is another
method for feature selection, which is: hybrid method. Hybrid method combines both the
filter and wrapper methods. Here I am going to use the hybrid method for selecting
features and I will show that hybrid method can get the same or more accurate result using
less features.
Results: The final result is showing that, in some cases hybrid method is giving the same
results as filter and wrapper method. Some other cases show that hybrid method is giving
more accurate result that filter and wrapper method. In all the cases hybrid method is
using less features than filter and wrapper method. Here filter and wrapper method is
using four features each and hybrid method is three features.
