MARC details
| 000 -LEADER |
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| fixed length control field |
03296ntm a2200289 a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
vtls000093771 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251117113316.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
160310t2015 da f ab m 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0001261(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
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| Level of rules in bibliographic description |
201905271507 |
| Level of effort used to assign nonsubject heading access points |
atie |
| Level of effort used to assign subject headings |
201710161506 |
| Level of effort used to assign classification |
aishah |
| -- |
201603101225 |
| -- |
asma |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMP |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FSKKP .S65 2015 r Thesis |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Soleh Ardiansyah |
| 245 10 - TITLE STATEMENT |
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| Title |
Rule extraction from multi-layer perceptron neural network using decision tree for currency exchange rates forecasting / |
| Statement of responsibility, etc. |
Soleh Ardiansyah |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
Kuantan, Pahang : |
| Name of publisher, distributor, etc. |
UMP, |
| Date of publication, distribution, etc. |
2015 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvii, 109 p. : |
| Other physical details |
ill. col. ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 500 ## - GENERAL NOTE |
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| General note |
Faculty of Computer System and Software Engineering |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Master of Science (Computer)) -- Universiti Malaysia Pahang – 2015 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Bibliography : p. 81-86 |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Neural network can be used in acquiring hidden knowledge in datasets. However, knowledge acquired by neural network was presented in its topology, the weights on the connections and by the activation functions of the hidden and output nodes. These representations are not easily understandable since neural networks act as a black box. The black box problem can be solved by extracting rule from trained neural network. Thus, the aim of this study was to extract valuable information (rule) from trained multi-layer perceptron (MLP) neural networks using decision tree. The main process in extracting rules from MLP using decision tree for currency exchange rate forecasting can be divided into two stages. In the first stage, the MLP network was built based on the parameter that was defined in the previous chapter. We also perform training and testing process experimentally and then the performance was evaluated in order to obtain the network with the best performance. The MLP network which provides the best prediction performance will be extracted by decision tree in the second stage by mapping input-output of the network directly. The forecasting result have shown that MLP network of EUR/USD produced a significant results compared to MLP network of GBP/USD and USD/JPY in term of MSE, RMSE, MAPE, and DS. It is quite evident that as the number of hidden neurons increases, MSE and MAPE decrease. In addition, the number of iterations for each model continues to increase along with the increasing number of hidden neurons. The results on decision tree induction show that C4.5 algorithm induction produced a significant result in term of accuracy 84.07% - 86.34%, precision and recall 93.17% and 81.97% respectively. This study has shown how rule can be extracted from MLP network by decision tree without making any assumptions about the networks activations function or having initial knowledge about the problem domain. The extracted rule can be used to explain the process of the neural network systems and also can be used in other systems like expert systems |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Computer System and Software Engineering |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Universities and Colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Theses |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="http://ecollib.ump.edu.my/3569/">http://ecollib.ump.edu.my/3569/</a> |
| Public note |
Library access only |