Graduation Year

2010

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Computer Science and Engineering

Major Professor

Dmitry Goldgof, Ph.D.

Co-Major Professor

Lawrence O. Hall, Ph.D.

Committee Member

Sudeep Sarkar, Ph.D.

Committee Member

Scott Samson, Ph.D.

Committee Member

Andrew Remsen, Ph.D.

Keywords

Marine Science, PICES, Machine Learning, Feature Selection, Support Vector Machine, SVM, Multi-Class, Pair-Wise

Abstract

Plankton imaging systems such as SIPPER produce a large quantity of data in the form of plankton images from a variety of classes. A system known as PICES was developed to quickly extract, classify and manage the millions of images produced from a single one-week research cruise. A new fast technique for parameter tuning and feature selection for Support Vector Machines using Wrappers was created. This technique allows for faster feature selection, while at the same time maintaining and sometimes improving classification accuracy. It also gives the user greater flexibility in the management of class contents in existing training libraries.

Support vector machines are binary classifiers that can implement multi-class classifiers by creating a classifier for each possible combination of classes or for each class using a one class versus all strategy. Feature selection searches for a single set of features to be used by each of the binary classifiers. This ignores the fact that features that may be good discriminators for two particular classes might not do well for other class combinations. As a result, the feature selection process may not include these features in the common set to be used by all support vector machines. It is shown through experimentation that by selecting features for each binary class combination, overall classification accuracy can be improved and the time required for training a multi-class support vector machine can be reduced. Another benefit of this approach is that significantly less time is required for feature selection when additional classes are added to the training data. This is because the features selected for the existing class combinations are still valid, so that feature selection only needs to be run for the new combination added.

This work resulted in a system called PICES, a GUI based user friendly system, which aids in the classification management of over 55 million images of plankton split amongst 180 classes. PICES embodies an improved means of performing Wrapper based feature selection that creates classifiers that train faster and are just as accurate and sometimes more accurate, while reducing the feature selection time.

Download

Share

COinS