Weed classification using genetic algorithm optimised classifiers

Wong, Wei Kitt (2016) Weed classification using genetic algorithm optimised classifiers. Doctoral thesis, Universiti Malaysia Sabah.

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Automated spot weeding with an efficient weed classification can increase production in crops and reduce herbicide usage. A proposed strategy of applying excessive feature sets followed by feature selection was applied on development of the classifiers to eliminate the non-discriminating features. Artificial Neural Network (ANN) and Support Vector Machines (SVM) were applied in the classification using a combination of image derived features. Optimising the classifier involves a tedious selection of subsets and parameters which can be considered a solution searching problem. Optimising the classifier parameters can be solved using heuristic methods such as Genetic Algorithm since it is a non - convex optimisation problem. In ANN structures, the features subset (input numbers) and hidden neuron layer are configurable while for SVM, the hyper parameter and the feature subset are configurable. In order to optimise the structures, feature subset and parameters, two optimisation approach were considered. These two optimisation approach include using backward Sequential Feature Selection (SFS) and Genetic Algorithm (GA) approach. GA requires a careful design of chromosome and fitness function in representing the structure, parameters and feature sets. In the fitness function for SVM optimisation, the fitness score is weighted between feature reduction term and fitness evaluation term of the candidate solution. For the SVMs optimised with GA, it was observed that all the GA configurations yielded better results (both on validation/test sets) as compared to SFS optimised counterpart. The results suggest that optimisation fitness function for SVM requires a simultaneous selection of feature subset /hyper parameters and a small value of weightage (between 0% to 20%) of the total fitness score should be allocated from the feature reduction term to avoid over fitting to training sets. As for the ANN optimisation using GA, fitness function (which includes the error reduction term, feature reduction term and neuron reduction term) showed lesser generalization with independent test sets in comparison with the SFS optimisation approach. The ANN configuration with SFS feature selection gave best results on validation error therefore showing better subset selection using SFS algorithm as compared to GA selection.

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: Artificial Neural Network (ANN, Support Vector Machines (SVM), classifier, Genetic Algorithm, Automated spot weeding
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: FACULTY > Faculty of Engineering
Depositing User: Munira
Date Deposited: 27 Oct 2017 08:11
Last Modified: 27 Oct 2017 08:11
URI: http://eprints.ums.edu.my/id/eprint/12163

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