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Cost of misclassification in software quality models
Title
Cost of misclassification in software quality models.
Creator
Guan, Xin., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description
Reliability has become a very important and competitive factor for software products. Using software quality models based on software measurements provides a systematic and scientific way to detect software faults early and to improve software reliability. This thesis considers several classification techniques including Generalized Classification Rule, MetaCost algorithm, Cost-Boosting algorithm and AdaCost algorithm. We also introduce the weighted logistic regression algorithm, and a new...
Show moreReliability has become a very important and competitive factor for software products. Using software quality models based on software measurements provides a systematic and scientific way to detect software faults early and to improve software reliability. This thesis considers several classification techniques including Generalized Classification Rule, MetaCost algorithm, Cost-Boosting algorithm and AdaCost algorithm. We also introduce the weighted logistic regression algorithm, and a new method to evaluate the performance of classification models---ROC Analysis. We focus our experiments on a very large legacy telecommunications system (LLTS) to build software quality models with principal components analysis. Two other data sets, CCCS and LTS are also used in our experiments.
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Date Issued
2000
PURL
http://purl.flvc.org/fcla/dt/15762
Subject Headings
Computer software--Quality control, Software measurement, Computer software--Testing
Format
Document (PDF)
2D/3D face recognition
Title
2D/3D face recognition.
Creator
Guan, Xin., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
This dissertation introduces our work on face recognition using a novel approach based on creating 3D face model from 2D face images. Together with the pose angle estimation and illumination compensation, this method can be used successfully to recognize 2D faces with 3D recognition algorithms. The results reported here were obtained partially with our own face image database, which had 2D and 3D face images of 50 subjects, with 9 different pose angles. It is shown that by applying even the...
Show moreThis dissertation introduces our work on face recognition using a novel approach based on creating 3D face model from 2D face images. Together with the pose angle estimation and illumination compensation, this method can be used successfully to recognize 2D faces with 3D recognition algorithms. The results reported here were obtained partially with our own face image database, which had 2D and 3D face images of 50 subjects, with 9 different pose angles. It is shown that by applying even the simple PCA algorithm, this new approach can yield successful recognition rates using 2D probing images and 3D gallery images. The insight gained from the 2D/3D face recognition study was also extended to the case of involving 2D probing and 2D gallery images, which offers a more flexible approach since it is much easier and practical to acquire 2D photos for recognition. To test the effectiveness of the proposed approach, the public AT&T face database, which had 2D only face photos of 40 subjects, with 10 different images each, was utilized in the experimental study. The results from this investigation show that with our approach, the 3D recognition algorithm can be successfully applied to 2D only images. The performance of the proposed approach was further compared with some of the existing face recognition techniques. Studies on imperfect conditions such as domain and pose/illumination variations were also carried out. Additionally, the performance of the algorithms on noisy photos was evaluated. Pros and cons of the proposed face recognition technique along with suggestions for future studies are also given in the dissertation.
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Date Issued
2012
PURL
http://purl.flvc.org/FAU/3342104
Subject Headings
Pattern recognition systems, Optical pattern recognition, Biometric identification, Face perception, Artificial intellingence
Format
Document (PDF)