Enhancement web proxy cache performance using wrapper feature selection methods with NB and J48
Web proxy cache technique reduces response time by storing a copy of pages between client and server sides. If requested pages are cached in the proxy, there is no need to access the server. Due to the limited size and excessive cost of cache compared to the other storages, cache replacement algo...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IOP Publishing
2017
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/59491/ http://irep.iium.edu.my/59491/ http://irep.iium.edu.my/59491/ http://irep.iium.edu.my/59491/7/59491-Enhancement%20web%20proxy%20cache.pdf http://irep.iium.edu.my/59491/13/Enhancement%20web%20proxy%20cache%20performance%20using%20wrapper%20feature%20selection%20methods%20with%20NB%20and%20J48.pdf |
| Summary: | Web proxy cache technique reduces response time by storing a copy of pages
between client and server sides. If requested pages are cached in the proxy, there is no need to
access the server. Due to the limited size and excessive cost of cache compared to the other
storages, cache replacement algorithm is used to determine evict page when the cache is full.
On the other hand, the conventional algorithms for replacement such as Least Recently Use
(LRU), First in First Out (FIFO), Least Frequently Use (LFU), Randomized Policy etc. may
discard important pages just before use. Furthermore, using conventional algorithm cannot be
well optimized since it requires some decision to intelligently evict a page before replacement.
Hence, most researchers propose an integration among intelligent classifiers and replacement
algorithm to improves replacement algorithms performance. This research proposes using
automated wrapper feature selection methods to choose the best subset of features that are
relevant and influence classifiers prediction accuracy. The result present that using wrapper
feature selection methods namely: Best First (BFS), Incremental Wrapper subset
selection(IWSS)embedded NB and particle swarm optimization(PSO)reduce number of
features and have a good impact on reducing computation time. Using PSO enhance NB
classifier accuracy by 1.1%, 0.43% and 0.22% over using NB with all features, using BFS and
using IWSS embedded NB respectively. PSO rises J48 accuracy by 0.03%, 1.91 and 0.04%
over using J48 classifier with all features, using IWSS-embedded NB and using BFS
respectively. While using IWSS embedded NB fastest NB and J48 classifiers much more than
BFS and PSO. However, it reduces computation time of NB by 0.1383 and reduce computation
time of J48 by 2.998. |
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