The Use of Background Knowledge in Decision Tree Induction
Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Data mining: concepts and techniques
Data mining: concepts and techniques
Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
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This paper presents the application of multi dimensional feature reduction of Principal Component Analysis (PCA) and Support Vector Machine (SVM) classifier for imaging surveillance system. Recently, research in image processing has raised much interest in the security surveillance systems community. Weapon detection is one of the greatest challenges facing by the community recently. In order to overcome this issue, application of the popularly used SVM classifier is performed to focus on the need of detecting dangerous weapons. However, PCA is used to explore the usefulness of each feature and reduce the multi dimensional features to simplified features with no underlying hidden structure. In this paper, we take advantage of the simplified features and classifier to categorize images object with the hope to detect dangerous weapons effectively. In order to validate the effectiveness of the SVM classifier, several classifiers are used to compare the overall accuracy and computational speed of the system with the compliment from the features reduction of PCA. These classifiers include Neural Network, Decision Trees, Naïve Bayes and k-Nearest Neighbor methods. The final outcome of this research clearly indicates that SVM has the ability in improving the classification accuracy using the extracted features. Besides, it is also shown that PCA is able to speed-up the computational time with the reduced dimensionality of the features while maintaining the classification accuracy.