Current Search:   Heredia, Brian (x)

View All Items

  • CSV Spreadsheet
(1 - 2 of 2)
Effects of gene selection and data sampling on prediction of breast cancer treatments
Title
Effects of gene selection and data sampling on prediction of breast cancer treatments.
Creator
Heredia, Brian, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
In recent years more and more researchers have begun to use data mining and machine learning tools to analyze gene microarray data. In this thesis we have collected a selection of datasets revolving around prediction of patient response in the specific area of breast cancer treatment. The datasets collected in this paper are all obtained from gene chips, which have become the industry standard in measurement of gene expression. In this thesis we will discuss the methods and procedures used in...
Show moreIn recent years more and more researchers have begun to use data mining and machine learning tools to analyze gene microarray data. In this thesis we have collected a selection of datasets revolving around prediction of patient response in the specific area of breast cancer treatment. The datasets collected in this paper are all obtained from gene chips, which have become the industry standard in measurement of gene expression. In this thesis we will discuss the methods and procedures used in the studies to analyze the datasets and their effects on treatment prediction with a particular interest in the selection of genes for predicting patient response. We will also analyze the datasets on our own in a uniform manner to determine the validity of these datasets in terms of learning potential and provide strategies for future work which explore how to best identify gene signatures.
Show less
Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004292, http://purl.flvc.org/fau/fd/FA00004292
Subject Headings
Antineoplastic agents -- Development, Breast -- Cancer -- Treatment, Cancer -- Genetic aspects, DNA mircroarrays, Estimation theory, Gene expression
Format
Document (PDF)
Machine Learning Algorithms for the Analysis of Social Media and Detection of Malicious User Generated Content
Title
Machine Learning Algorithms for the Analysis of Social Media and Detection of Malicious User Generated Content.
Creator
Heredia, Brian, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
One of the de ning characteristics of the modern Internet is its massive connectedness, with information and human connection simply a few clicks away. Social media and online retailers have revolutionized how we communicate and purchase goods or services. User generated content on the web, through social media, plays a large role in modern society; Twitter has been in the forefront of political discourse, with politicians choosing it as their platform for disseminating information, while...
Show moreOne of the de ning characteristics of the modern Internet is its massive connectedness, with information and human connection simply a few clicks away. Social media and online retailers have revolutionized how we communicate and purchase goods or services. User generated content on the web, through social media, plays a large role in modern society; Twitter has been in the forefront of political discourse, with politicians choosing it as their platform for disseminating information, while websites like Amazon and Yelp allow users to share their opinions on products via online reviews. The information available through these platforms can provide insight into a host of relevant topics through the process of machine learning. Speci - cally, this process involves text mining for sentiment analysis, which is an application domain of machine learning involving the extraction of emotion from text. Unfortunately, there are still those with malicious intent and with the changes to how we communicate and conduct business, comes changes to their malicious practices. Social bots and fake reviews plague the web, providing incorrect information and swaying the opinion of unaware readers. The detection of these false users or posts from reading the text is di cult, if not impossible, for humans. Fortunately, text mining provides us with methods for the detection of harmful user generated content. This dissertation expands the current research in sentiment analysis, fake online review detection and election prediction. We examine cross-domain sentiment analysis using tweets and reviews. Novel techniques combining ensemble and feature selection methods are proposed for the domain of online spam review detection. We investigate the ability for the Twitter platform to predict the United States 2016 presidential election. In addition, we determine how social bots in uence this prediction.
Show less
Date Issued
2018
PURL
http://purl.flvc.org/fau/fd/FA00013067
Subject Headings
Machine learning., Text mining., User-generated content., Social media.
Format
Document (PDF)