Adaptive prototype selection for k-nearest neighbors classfication using geometric median

Name: Chatchai Kasemtaweechok

LCSH: Kasetsart University -- Dissertations. Ph.D. (Computer Science) 2019

Classification :.LCCS: QA76.9.A43

LCSH: Kasetsart University. -- Department of Computer Science -- Dissertations

LCSH: Computer algorithms

LCSH: Nearest neighbor analysis (Statistics)

Abstract: The k-nearest neighbors (kNN) algorithm is one of the most popular and simplest lazy learners. However, as the training dataset becomes larger, the algorithm suffers from the following drawbacks: large storage requirements, slow classification speed, and high sensitivity to noise. To overcome these drawbacks, we reduce the size of the training data by only selecting the necessary prototypes before the classification. This study proposes an extended prototype selection technique based on the geometric median (GM). We compare the proposed method with seven state-of-the-art prototype selection methods and 1NN as the baseline model. We use 25 datasets from the KEEL and UCI dataset repository website. The proposed method runs at least 3.5 times faster than the baseline model at the cost of slightly reduced accuracy. In addition, the classification accuracy and kappa value of the proposed method are comparable to those of all the state-of-the-art prototype selection methods considered.

Kasetsart University. Office of the University Library

Address: Bangkok

Email: tdckulib@ku.ac.th

Name: Worasait Suwannik

Role: Thesis Advisor

Created: 2019

Modified: 2025-06-29

Issued: 2025-06-29

วิทยานิพนธ์/Thesis

application/pdf

URL: https://www.lib.ku.ac.th/KUthesis/2562/chatchai-kas-all.pdf

CallNumber: QA76.9.A43 .C43

eng

DegreeName: Doctor of Philosophy

Level: Doctoral Degree

Descipline: Computer Science

Grantor: Kasetsart University

©copyrights Kasetsart University

ลำดับที่.	ชื่อแฟ้มข้อมูล	ขนาดแฟ้มข้อมูล	จำนวนเข้าถึง	วัน-เวลาเข้าถึงล่าสุด
1	chatchai-kas-all.pdf	2.1 MB

ใช้เวลา

0.021841 วินาที