Grey Wolf Optimization for One-Against-One Multi-class Support Vector Machines

Citation:
Esraa Elhariri, N. El-Bendary, and A. A. Aboul Ella Hassanien, "Grey Wolf Optimization for One-Against-One Multi-class Support Vector Machines", 7th IEEE International Conference of Soft Computing and Pattern Recognition, , Kyushu University, Fukuoka, Japan, , November 13 - 15, 2015.

Date Presented:

November 13 - 15

Abstract:

Grey Wolf Optimization (GWO) algorithm is a
new meta-heuristic method, which is inspired by grey wolves,
to mimic the hierarchy of leadership and grey wolves hunting
mechanism in nature. This paper presents a hybrid model that
employs grey wolf optimizer (GWO) along with support vector
machines (SVMs) classification algorithm to improve the classification
accuracy via selecting the optimal settings of SVMs
parameters. The proposed approach consists of three phases;
namely pre-processing, feature extraction, and GWO-SVMs
classification phases. The proposed classification approach was
implemented by applying resizing, remove background, and
extracting color components for each image. Then, feature
vector generation has been implemented via applying PCA
feature extraction. Finally, GWO-SVMs model is developed
for selecting the optimal SVMs parameters. The proposed
approach has been implemented via applying One-againstOne
multi-class SVMs system using 3-fold cross-validation. The
datasets used for experiments were constructed based on real
sample images of bell pepper at different stages, which were
collected from farms in Minya city, Upper Egypt. Datasets
of total 175 images were used for both training and testing
datasets. Experimental results indicated that the proposed
GWO-SVMs approach achieved better classification accuracy
compared to the typical SVMs classification algorithm.

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