Improveing F-beta Score in Classifying Shark Data into Shark Behaviors
type: CreativeWork
description: One metric used to measure classification performance in machine learning is F-beta score. The objective in this thesis is to improve the average F-b score computed in classifying shark data into shark behaviors, namely; Resting, Swimming, Feeding, and Non-Directed Motion (NDM). Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) are utilized to balance the data, from which pre-processed Fast Fourier Transform (FFT), Walsh-Hadamard Transform (WHT), and Autocorrelation (AC) features are extracted then classified using Convolutional Neural Network (CNN) and K-Nearest Neighbors (K-NN). All the combinations of the two balancing techniques, the three feature types, and the two machine learning algorithms are applied then compared to examine the average F-beta score improvement. Other signal processing techniques are also applied, to reduce the noise level of the recorded raw shark data and enhance its Signal-to-Noise Ratio (SNR). The average F-beta scores showed that K-NN performed at its best when using FFT-only features while CNN performed at its best when using WHT-FFT features. In the K-NN case, FFT performed better when it was used alone than when it was combined with any other feature type. On the other hand, WHT performed better when it was combined with any other feature type than when it was used alone. In the CNN case, WHT and FFT performed better together than they did separately. In other words, Combining FFT and WHT features in CNN resulted in considerably improved average F-beta score, while combining them in K-NN averaged their scores. Also, whether alone or combined with other feature types, AC did not work well in CNN as it resulted in poor average F-beta scores. In K-NN, combining AC with other feature types did not improve the average F-beta score from when it is used alone. The average F-beta scores also showed that reducing the data imbalance nature during the pre-processing phase is more effective than mitigati…
abstract: One metric used to measure classification performance in machine learning is F-beta score. The objective in this thesis is to improve the average F-b score computed in classifying shark data into shark behaviors, namely; Resting, Swimming, Feeding, and Non-Directed Motion (NDM). Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) are utilized to balance the data, from which pre-processed Fast Fourier Transform (FFT), Walsh-Hadamard Transform (WHT), and Autocorrelation (AC) features are extracted then classified using Convolutional Neural Network (CNN) and K-Nearest Neighbors (K-NN). All the combinations of the two balancing techniques, the three feature types, and the two machine learning algorithms are applied then compared to examine the average F-beta score improvement. Other signal processing techniques are also applied, to reduce the noise level of the recorded raw shark data and enhance its Signal-to-Noise Ratio (SNR). The average F-beta scores showed that K-NN performed at its best when using FFT-only features while CNN performed at its best when using WHT-FFT features. In the K-NN case, FFT performed better when it was used alone than when it was combined with any other feature type. On the other hand, WHT performed better when it was combined with any other feature type than when it was used alone. In the CNN case, WHT and FFT performed better together than they did separately. In other words, Combining FFT and WHT features in CNN resulted in considerably improved average F-beta score, while combining them in K-NN averaged their scores. Also, whether alone or combined with other feature types, AC did not work well in CNN as it resulted in poor average F-beta scores. In K-NN, combining AC with other feature types did not improve the average F-beta score from when it is used alone. The average F-beta scores also showed that reducing the data imbalance nature during the pre-processing phase is more effective than mitigati…
author: Ali, Ibrahim M
keywords: Activity Recognition, CNN K-NN, Digital Signal Processing, Imbalanced data, Machine Learning, SMOTE ADASYN, Engineering, Mathematics
date published: 2024-01-01T08:00:00Z
date modified: 2026-07-24T01:40:36Z
provider: Claremont Graduate University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Claremont Graduate University