MARC details
| 000 -LEADER |
|---|
| fixed length control field |
04076ntm a2200373 a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251125110727.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
|---|
| fixed length control field |
a||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
230410t20222022my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0009590 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMP |
| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FKOM .F37 2022 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Nur Farahaina Binti Idris, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd / |
| Statement of responsibility, etc. |
Nur Farahaina Binti Idris |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMP, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2022 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xi, 145 pages : |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
computer |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
computer disc |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Faculty of Computing |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (Master of Science) -- Universiti Malaysia Pahang – 2022 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Classification is a data mining technique used to classify varied data types according to a specific criterion. One of the most powerful machine learning methods to handle classification problems is the decision tree. There are various decision tree algorithms, but the most commonly used are Iterative Dichotomiser 3 (ID3), CART, and C4.5. ID3 has the most advantages among the three algorithms, especially in processing time, as it builds the fastest tree with short depth. However, despite the decision tree’s commonness in handling classification problems, it suffers problems like high variance and overfitting, leading to poor generalisation. The combination of fuzzy and ID3 algorithm manages the data more efficiently as it combines both the advantages of fuzzy and decision tree. For the proposed technique of the FID3-DBD algorithm, the continuous and discrete (integer) attributes would be defined in the linguistic values of the fuzzy sets, and the FUZZYDBD method is being used to set up the fuzzy sets’ parameters. Replacement with the linguistic labels of fuzzy sets with the highest compatibility of input values has also been done before the tree induction occurs. The proposed technique solves the limitation of the classic ID3 algorithm that cannot classify the continuous-valued attributes and, at the same time, increase the classification accuracy. The bagging method was then applied to the FID3-DBD algorithm to overcome overfitting problems and high variance in decision trees. Four breast cancer datasets were used to evaluate the classification accuracy: Wisconsin Breast Cancer (Original) dataset, WDBC (Diagnostic) dataset, Breast Cancer Coimbra dataset, and Mammographic Mass dataset. All those datasets were acquired from the UCI machine learning repository. This study aims to solve the limitation of the classic ID3 algorithm that is unable to classify continuous data well and overcome the high variance and overfitting issues. This research methodology consists of four fundamental steps: literature review, data collection, experiment implementation, and report writing. The FID3-DBD algorithm acquired the classification accuracy of 94.362% for the Wisconsin Breast Cancer (Original) dataset, 94.358% for the WDBC (Diagnostic) dataset, 81.119% for the Mammographic Mass dataset and 64.224% for the Coimbra dataset. The BFID3-DBD algorithm obtained the classification accuracy of 96.003% for the Wisconsin Breast Cancer (Original) dataset, 95.273% for the WDBC (Diagnostic) dataset, 81.590% for the Mammographic Mass dataset and 68.966% for the Coimbra dataset. The study verified that the FID3-DBD algorithm could classify the continuous data, and the BFID3-DBD algorithm overcame the overfitting issue, reduced high variance, and increased test data classification accuracy. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Faculty of Computing |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |