Feature selection using genetic algorithm
by Kanyanut Homsapaya
Title: | Feature selection using genetic algorithm |
Other title(s): | Feature selection using genetic algorithm |
Author(s): | Kanyanut Homsapaya |
Advisor: | Ohm Sornil |
Degree name: | Doctor of Philosophy |
Degree level: | Doctoral |
Degree discipline: | Computer Science and Information Systems |
Degree department: | School of Applied Statistics |
Degree grantor: | National Institute of Development Administration |
Issued date: | 2017 |
Digital Object Identifier (DOI): | 10.14457/NIDA.the.2017.43 |
Publisher: | National Institute of Development Administration |
Abstract: |
In this dissertation, a method of feature selection in machine learning, and
more particularly supervised learning is presented. Supervised learning is a machine
learning task that infers answers from a training data set. In machine learning, training
datasets are employed in order to create a model which enables reasonable
predictions, while in supervised learning, each training example is a training set
consisting of instances and labels, and the learning objective is to be able to predict
the label of a new unseen instance with as few errors as possible. In recent years,
many proposed learning algorithms that perform fairly well have been proposed. The
factors to accomplish successful model building depend on many aspects such as
noise and size of data. Most often for learning algorithms, it is assumed that training
data is represented by a vector of numerical data for which each measurement is a
feature, and an important question related to machine learning is how to represent
instances using vectors of these to yield high learning performance. |
Subject(s): | Genetic algorithm |
Keyword(s): | Supervised learning
Machine learning |
Resource type: | Dissertation |
Extent: | 110 leaves |
Type: | Text |
File type: | application/pdf |
Language: | eng |
Rights: | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
URI: | https://repository.nida.ac.th/handle/662723737/5874 |
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