Subramanaim, Jayaprakash (2016) Higher Dimensional Feature Reduction using Hybrid Particle Swarm Optimization. Masters thesis, Dublin, National College of Ireland.
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Digital informations are tremendously increasing at every second, in the recent decade there has been a surplus amount of data storing day by day. With the advancement in efficient machine learning algorithms, analyzing of complex data becomes much easier. Categorizing these textual data recently became more important for the organizations dealing with huge data. Handling higher dimensionality features becomes more complicated on process huge data, forcing to select certain Features (FS) which are higher values for the process. In this project an efficient feature selection algorithm which is based on Hybrid Particle Swarm Optimization (PSO) is used improve the efficiency of Feature Selection process. Here Support Vector Machine is used as the classifier in combination with PSO to give a best features.
|Item Type:||Thesis (Masters)|
|Subjects:||Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
|Divisions:||School of Computing > Master of Science in Data Analytics|
|Depositing User:||CAOIMHE NI MHAICIN|
|Date Deposited:||27 Jan 2017 13:32|
|Last Modified:||27 Jan 2017 13:32|
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