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An Iterative Feature Perturbation Method For Gene Selection From Microarray Data, Juana Canul Reich

USF Tampa Graduate Theses and Dissertations

Gene expression microarray datasets often consist of a limited number of samples relative to a large number of expression measurements, usually on the order of thousands of genes. These characteristics pose a challenge to any classification model as they might negatively impact its prediction accuracy. Therefore, dimensionality reduction is a core process prior to any classification task.

This dissertation introduces the iterative feature perturbation method (IFP), an embedded gene selector that iteratively discards non-relevant features. IFP considers relevant features as those which after perturbation with noise cause a change in the predictive accuracy of the classification model. Non-relevant features do …

Statistical Learning And Behrens-Fisher Distribution Methods For Heteroscedastic Data In Microarray Analysis, Nabin K. Manandhr-Shrestha

USF Tampa Graduate Theses and Dissertations

The aim of the present study is to identify the di®erentially expressed genes be- tween two di®erent conditions and apply it in predicting the class of new samples using the microarray data. Microarray data analysis poses many challenges to the statis- ticians because of its high dimensionality and small sample size, dubbed as "small n large p problem". Microarray data has been extensively studied by many statisticians and geneticists. Generally, it is said to follow a normal distribution with equal vari- ances in two conditions, but it is not true in general. Since the number of replications is very small, …

Background Subtraction Using Ensembles Of Classifiers With An Extended Feature Set, Brendan F. Klare

USF Tampa Graduate Theses and Dissertations

The limitations of foreground segmentation in difficult environments using standard color space features often result in poor performance during autonomous tracking. This work presents a new approach for classification of foreground and background pixels in image sequences by employing an ensemble of classifiers, each operating on a different feature type such as the three RGB features, gradient magnitude and orientation features, and eight Haar features. These thirteen features are used in an ensemble classifier where each classifier operates on a single image feature. Each classifier implements a Mixture of Gaussians-based unsupervised background classification algorithm. The non-thresholded, classification decision score of …

Learning From Spatially Disjoint Data, Divya Bhadoria

USF Tampa Graduate Theses and Dissertations

Committees of classifiers, also called mixtures or ensembles of classifiers, have become popular because they have the potential to improve on the performance of a single classifier constructed from the same set of training data. Bagging and boosting are some of the better known methods of constructing a committee of classifiers. Committees of classifiers are also important because they have the potential to provide a computationally scalable approach to handling massive datasets. When the emphasis is on computationally scalable approaches to handling massive datasets, the individual classifiers are often constructed from a small faction of the total data. In this …

Graph-Theoretic Techniques For Web Content Mining, Adam Schenker

USF Tampa Graduate Theses and Dissertations

In this dissertation we introduce several novel techniques for performing data mining on web documents which utilize graph representations of document content. Graphs are more robust than typical vector representations as they can model structural information that is usually lost when converting the original web document content to a vector representation. For example, we can capture information such as the location, order and proximity of term occurrence, which is discarded under the standard document vector representation models. Many machine learning methods rely on distance computations, centroid calculations, and other numerical techniques. Thus many of these methods have not been applied …

Fourier-Transform Infrared Spectroscopic Imaging Of Prostate Histopathology, Daniel Celestino Fernandez

USF Tampa Graduate Theses and Dissertations

Vibrational spectroscopic imaging techniques have emerged as powerful methods of obtaining sensitive spatially resolved molecular information from microscopic samples. The data obtained from such techniques reflect the intrinsic molecular chemistry of the sample and in particular yield a wealth of information regarding functional groups which comprise the majority of important molecules found in cells and tissue. These spectroscopic imaging techniques also have the advantage of acquisition of large numbers of spectral measurements which allow statistical analysis of spectral features which are characteristic of the normal histological state as well as different pathologic disease states. Databases of large numbers of samples …