Introducing Fault Tolerance to XCS
碩士 === 國立交通大學 === 資訊科學與工程研究所 === 103 === In this paper, we introduce fault tolerance to XCS and propose a new XCS framework called XCS with Fault Tolerance (XCS/FT). As an important branch of learning classifier systems, XCS has been proven capable of evolving maximally accurate, maximally general p...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | en_US |
Published: |
2015
|
Online Access: | http://ndltd.ncl.edu.tw/handle/87564032570499504048 |
id |
ndltd-TW-103NCTU5394142 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-103NCTU53941422016-08-12T04:14:07Z http://ndltd.ncl.edu.tw/handle/87564032570499504048 Introducing Fault Tolerance to XCS 引入容錯機制進入XCS分類器 Chen, Hong-Wei 陳宏偉 碩士 國立交通大學 資訊科學與工程研究所 103 In this paper, we introduce fault tolerance to XCS and propose a new XCS framework called XCS with Fault Tolerance (XCS/FT). As an important branch of learning classifier systems, XCS has been proven capable of evolving maximally accurate, maximally general problem solutions. However, in practice, it oftentimes generates a lot of rules, which lower the readability of the evolved classification model, and thus, people may not be able to get the desired knowledge or useful information out of the model. Inspired by the fault tolerance mechanism proposed in field of data mining, we devise a new XCS framework by integrating the concept and mechanism of fault tolerance into XCS in order to reduce the number of classification rules and therefore to improve the readability of the generated prediction model. The workflow and operations of the XCS/FT framework are described in detail. A series of N-multiplexer experiments, including 6-bit, 11-bit, 20-bit, and 37-bit multiplexers, are conducted to examine whether XCS/FT can accomplish its goal of design. According to the experimental results, XCS/FT can offer the same level of prediction accuracy on the test problems as XCS can, while the prediction model Chen, Ying-Ping 陳穎平 2015 學位論文 ; thesis 32 en_US |
collection |
NDLTD |
language |
en_US |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 國立交通大學 === 資訊科學與工程研究所 === 103 === In this paper, we introduce fault tolerance to XCS and propose a new XCS framework called XCS with Fault Tolerance (XCS/FT). As an important branch of learning classifier systems, XCS has been proven capable of evolving maximally accurate, maximally general problem solutions. However, in practice, it oftentimes generates a lot of rules, which lower the readability of the evolved classification model, and thus, people may not be able to get the desired knowledge or useful information out of the model. Inspired by the fault tolerance mechanism proposed in field of data mining, we devise a new XCS framework by integrating the concept and mechanism of fault tolerance into XCS in order to reduce the number of classification rules and therefore to improve the readability of the generated prediction model. The workflow and operations of the XCS/FT framework are described in detail. A series of N-multiplexer experiments, including 6-bit, 11-bit, 20-bit, and 37-bit multiplexers, are conducted to examine whether XCS/FT can accomplish its goal of design. According to the experimental results, XCS/FT can offer the same level of prediction accuracy on the test problems as XCS can, while the prediction model
|
author2 |
Chen, Ying-Ping |
author_facet |
Chen, Ying-Ping Chen, Hong-Wei 陳宏偉 |
author |
Chen, Hong-Wei 陳宏偉 |
spellingShingle |
Chen, Hong-Wei 陳宏偉 Introducing Fault Tolerance to XCS |
author_sort |
Chen, Hong-Wei |
title |
Introducing Fault Tolerance to XCS |
title_short |
Introducing Fault Tolerance to XCS |
title_full |
Introducing Fault Tolerance to XCS |
title_fullStr |
Introducing Fault Tolerance to XCS |
title_full_unstemmed |
Introducing Fault Tolerance to XCS |
title_sort |
introducing fault tolerance to xcs |
publishDate |
2015 |
url |
http://ndltd.ncl.edu.tw/handle/87564032570499504048 |
work_keys_str_mv |
AT chenhongwei introducingfaulttolerancetoxcs AT chénhóngwěi introducingfaulttolerancetoxcs AT chenhongwei yǐnrùróngcuòjīzhìjìnrùxcsfēnlèiqì AT chénhóngwěi yǐnrùróngcuòjīzhìjìnrùxcsfēnlèiqì |
_version_ |
1718374423157800960 |