Title :
Investigation of evolutionary feature subset selection in multi-temporal datasets for harmful algal bloom detection
Author :
Gokaraju, Balakrishna ; Durbha, Surya S. ; King, Roger L. ; Younan, Nicolas H.
Author_Institution :
Center for Adv. Vehicular Syst. (CAVS), Mississippi State Univ., Starkville, MS, USA
Abstract :
In the present study we investigate the evolutionary feature subset selection using wrapper based genetic algorithms on Multi-temporal datasets. Feature subset selection helps in reducing the original feature dimension and also yields high performance. The evolutionary strategy attains a global optimum by reducing the computations iteratively and by traversing intelligently in the entire feature space. This method gave a very high performance improvement up to 0.97 kappa accuracy with a best reduced feature dimension for harmful algal bloom detection.
Keywords :
data mining; genetic algorithms; geophysical image processing; microorganisms; oceanographic techniques; remote sensing; support vector machines; evolutionary feature subset selection; feature space; harmful algal bloom detection; multitemporal datasets; original feature dimension reduction; wrapper based genetic algorithms; Accuracy; Computational modeling; Data mining; Feature extraction; Genetic algorithms; Indexes; Machine learning; Feature Selection; Genetic Algorithms; Multi-Temporal; Support Vector Machines;
Conference_Titel :
Analysis of Multi-temporal Remote Sensing Images (Multi-Temp), 2011 6th International Workshop on the
Conference_Location :
Trento
Print_ISBN :
978-1-4577-1202-9
DOI :
10.1109/Multi-Temp.2011.6005070
