Multi-Objective Gray-Wolf Optimization for Attribute Reduction

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86096570" target="_blank" >RIV/61989100:27240/15:86096570 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.procs.2015.09.006" target="_blank" >http://dx.doi.org/10.1016/j.procs.2015.09.006</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.procs.2015.09.006" target="_blank" >10.1016/j.procs.2015.09.006</a>

Result language
angličtina
Original language name
Multi-Objective Gray-Wolf Optimization for Attribute Reduction
Original language description
Feature sets are always dependent, redundant and noisy in almost all application domains. These problems in The data always declined the performance of any given classifier as it make it difficult for the training phase to converge effectively and it affect also the running time for classification at operation and training time. In this work a system for feature selection based on multi-objective gray wolf optimization is proposed. The existing methods for feature selection either depend on the data description; filter-based methods, or depend on the classifier used; wrapper approaches. These two main approaches lakes of good performance and data description in the same system. In this work gray wolf optimization; a swarm-based optimization method, wasemployed to search the space of features to find optimal feature subset that both achieve data description with minor redundancy and keeps classification performance. At the early stages of optimization gray wolf uses filter-based princi
Czech name
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Czech description
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Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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