Engineering Commons^™

Stability And Classification Performance Of Feature Selection Techniques, Huanjing Wang, Taghi Khoshgoftaar, Qianhui Liang

Computer Science Faculty Publications

Feature selection techniques can be evaluated based on either model performance or the stability (robustness) of the technique. The ideal situation is to choose a feature selec- tion technique that is robust to change, while also ensuring that models built with the selected features perform well. One domain where feature selection is especially important is software defect prediction, where large numbers of met- rics collected from previous software projects are used to help engineers focus their efforts on the most faulty mod- ules. This study presents a comprehensive empirical ex- amination of seven filter-based feature ranking techniques (rankers) applied to …

Classification For Mass Spectra And Comprehensive Two-Dimensional Chromatograms, Xue Tian

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Mass spectra contain characteristic information regarding the molecular structure and properties of compounds. The mass spectra of compounds from the same chemically related group are similar. Classification is one of the fundamental methodologies for analyzing mass spectral data. The primary goals of classification are to automatically group compounds based on their mass spectra, to find correlation between the properties of compounds and their mass spectra, and to provide a positive identification of unknown compounds.

This dissertation presents a new algorithm for the classification of mass spectra, the most similar neighbor with a probability-based spectrum similarity measure (MSN-PSSM). Experimental results demonstrate …

Design And Implementation Of A Byzantine Fault Tolerance Framework For Non-Deterministic Applications, H. Zhang, Wenbing Zhao, Louise E. Moser, P. Michael Melliar-Smith

Electrical and Computer Engineering Faculty Publications

State-machine-based replication is an effective way to increase the availability and dependability of mission-critical applications. However, all practical applications contain some degree of non-determinism. Consequently, ensuring strong replica consistency in the presence of application non-determinism has been one of the biggest challenges in building dependable distributed systems. In this Study, the authors propose a classification of common types of application non-determinism with respect to the requirement of achieving Byzantine fault tolerance (BFT), and present the design and implementation of a BFT framework that controls these types of non-determinism in a systematic manner.

Two-Class Classification With Various Characteristics Based On Kernel Principal Component Analysis And Support Vector Machines, Ivanna Kristianti Timotius, Iwan Setyawan, Andreas Ardian Febrianto

Makara Journal of Technology

Two class pattern classification problems appeared in many applications. In some applications, the characteristic of the members in a class is dissimilar. This paper proposed a classification system for this problem. The proposed system was developed based on the combination of kernel principal component analysis (KPCA) and support vector machines (SVMs). This system has been implemented in a two class face recognition problem. The average of the classification rate in this face image classification is 82.5%.

Algorithms For Training Large-Scale Linear Programming Support Vector Regression And Classification, Pablo Rivas Perea

Open Access Theses & Dissertations

The main contribution of this dissertation is the development of a method to train a Support Vector Regression (SVR) model for the large-scale case where the number of training samples supersedes the computational resources. The proposed scheme consists of posing the SVR problem entirely as a Linear Programming (LP) problem and on the development of a sequential optimization method based on variables decomposition, constraints decomposition, and the use of primal-dual interior point methods. Experimental results demonstrate that the proposed approach has comparable performance with other SV-based classifiers. Particularly, experiments demonstrate that as the problem size increases, the sparser the solution …

Measuring Traffic Flow And Classifying Vehicle Types: A Surveillance Video Based Approach, Erhan İnce

Turkish Journal of Electrical Engineering and Computer Sciences

The paper presents a vehicle counting method based on invariant moments and shadow aware foreground masks. Estimation of the background and the segmentation of foreground regions can be done using either the Mixture of Gaussians model (MoG) or an improved version of the Group Based Histogram (GBH) technique. The work demonstrates that, even though the improved GBH method delivers performance just as good as MoG, considering computational efficiency, MoG is more suitable. Shadow aware binary masks for each frame are formed by using background subtraction and shadow removal in the Hue Saturation and Value (HSV) domain. To determine new vehicles …