Digital Commons Network^™

Development Of A Workflow For The Comparison Of Classification Techniques, Zanifa Omary

Masters

As the interest in machine learning and data mining springs up, the problem of how to assess learning algorithms and compare classifiers become more pressing. This has been associated with the lack of comprehensive and complete workflow depending on the project scale to provide guidance to its users. This means the success or failure of the project can be highly dependent on the person or team carrying it. The standard practice adopted by many researchers and experimenters has been to follow steps or phases from existing workflows such as CRISP-DM, KDD and SASSEMMA. However, as machine learning and data mining …

Development Of A Workflow For The Comparison Of Classification Techniques, Zanifa Omary

Dissertations

As the interest in machine learning and data mining springs up, the problem of how to assess learning algorithms and compare classifiers become more pressing. This has been associated with the lack of comprehensive and complete workflow depending on the project scale to provide guidance to its users. This means the success or failure of the project can be highly dependent on the person or team carrying it.

The standard practice adopted by many researchers and experimenters has been to follow steps or phases from existing workflows such as CRISP-DM, KDD and SASSEMMA. However, as machine learning and data mining …

Classifier System Learning Of Good Database Schema, Mitsuru Tanaka

LSU New Orleans Theses and Dissertations

This thesis presents an implementation of a learning classifier system which learns good database schema. The system is implemented in Java using the NetBeans development environment, which provides a good control for the GUI components. The system contains four components: a user interface, a rule and message system, an apportionment of credit system, and genetic algorithms. The input of the system is a set of simple database schemas and the objective for the classifier system is to keep the good database schemas which are represented by classifiers. The learning classifier system is given some basic knowledge about database concepts or …

Clustering Via Supervised Support Vector Machines, Sepehr Merat

LSU New Orleans Theses and Dissertations

An SVM-based clustering algorithm is introduced that clusters data with no a priori knowledge of input classes. The algorithm initializes by first running a binary SVM classifier against a data set with each vector in the set randomly labeled. Once this initialization step is complete, the SVM confidence parameters for classification on each of the training instances can be accessed. The lowest confidence data (e.g., the worst of the mislabeled data) then has its labels switched to the other class label. The SVM is then re-run on the data set (with partly re-labeled data). The repetition of the above process …

Classification Algorithms On The Cell Processor, Mateusz Wyganowski

Theses

The rapid advancement in the capacity and reliability of data storage technology has allowed for the retention of virtually limitless quantity and detail of digital information. Massive information databases are becoming more and more widespread among governmental, educational, scientific, and commercial organizations. By segregating this data into carefully defined input (e.g.: images) and output (e.g.: classification labels) sets, a classification algorithm can be used develop an internal expert model of the data by employing a specialized training algorithm. A properly trained classifier is capable of predicting the output for future input data from the same input domain that it was …

Scaling Ant Colony Optimization With Hierarchical Reinforcement Learning Partitioning, Erik J. Dries, Gilbert L. Peterson

Faculty Publications

This paper merges hierarchical reinforcement learning (HRL) with ant colony optimization (ACO) to produce a HRL ACO algorithm capable of generating solutions for large domains. This paper describes two specific implementations of the new algorithm: the first a modification to Dietterich’s MAXQ-Q HRL algorithm, the second a hierarchical ant colony system algorithm. These implementations generate faster results, with little to no significant change in the quality of solutions for the tested problem domains. The application of ACO to the MAXQ-Q algorithm replaces the reinforcement learning, Q-learning, with the modified ant colony optimization method, Ant-Q. This algorithm, MAXQ-AntQ, converges to solutions …

Identifying Biological Echoes In Radar Scans Using Machine Learning, Reginald Mead, John Paxton, Richard S. Sojda

International Congress on Environmental Modelling and Software

Radar ornithology using data from the NEXRAD weather radar system has given scientists new tools for studying bird migration in the United States. Unfortunately, the process of identifying echoes from birds in radar data still largely requires that trained technicians spend hours manually scouring radar scans. This paper provides some background for understanding biological echoes in NEXRAD data and then describes our initial investigations of the use of machine learning techniques to help automate the process of echo classification. Doppler data allows researchers to look at bird migration by examining large clusters of birds that could be observed approaching and …

Detection Of Marine Vehicles In Images And Video Of Open Sea, Sergiy Fefilatyev

USF Tampa Graduate Theses and Dissertations

This work presents a new technique for automatic detection of marine vehicles in images and video of open sea. Users of such system include border guards, military, port safety, flow management, and sanctuary protection personnel. The source of images and video is a digital camera or a camcorder which is placed on a buoy or stationary mounted in a harbor facility. The system is intended to work autonomously, taking images of the surrounding ocean surface and analyzing them for the presence of marine vehicles. The goal of the system is to detect an approximate window around the ship. The proposed …

Assessing The Costs Of Sampling Methods In Active Learning For Annotation, James Carroll, Robbie Haertel, Peter Mcclanahan, Eric K. Ringger, Kevin Seppi

Faculty Publications

Traditional Active Learning (AL) techniques assume that the annotation of each datum costs the same. This is not the case when annotating sequences; some sequences will take longer than others. We show that the AL technique which performs best depends on how cost is measured. Applying an hourly cost model based on the results of an annotation user study, we approximate the amount of time necessary to annotate a given sentence. This model allows us to evaluate the effectiveness of AL sampling methods in terms of time spent in annotation. We acheive a 77% reduction in hours from a random …

Active Learning For E-Rulemaking: Public Comment Categorization, Stephen Purpura, Claire Cardie, Jesse Simons

Cornell e-Rulemaking Initiative Publications

We address the e-rulemaking problem of reducing the manual labor required to analyze public comment sets. In current and previous work, for example, text categorization techniques have been used to speed up the comment analysis phase of e-rulemaking - by classifying sentences automatically, according to the rule-specific issues [2] or general topics that they address [7, 8]. Manually annotated data, however, is still required to train the supervised inductive learning algorithms that perform the categorization. This paper, therefore, investigates the application of active learning methods for public comment categorization: we develop two new, general-purpose, active learning techniques to selectively sample …

Exploring The Characteristics Of Opinion Expressions For Political Opinion Classification, Bei Yu, Stefan Kaufmann, Daniel Diermeier

School of Information Studies - Faculty Scholarship

Recently there has been increasing interest in constructing general-purpose political opinion classifiers for applications in e-Rulemaking. This problem is generally modeled as a sentiment classification task in a new domain. However, the classification accuracy is not as good as that in other domains such as customer reviews. In this paper, we report the results of a series of experiments designed to explore the characteristics of political opinion expression which might affect the sentiment classification performance. We found that the average sentiment level of Congressional debate is higher than that of neutral news articles, but lower than that of movie reviews. …

Comparison Of Machine Learning Algorithms For Modeling Species Distributions: Application To Stream Invertebrates From Western Usa Reference Sites, Margi Dubal

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Machine learning algorithms are increasingly being used by ecologists to model and predict the distributions of individual species and entire assemblages of sites. Accurate prediction of distribution of species is an important factor in any modeling. We compared prediction accuracy of four machine learning algorithms-random forests, classification trees, support vector machines, and gradient boosting machines to a traditional method, linear discriminant models (LDM), on a large set of stream invertebrate data collected at 728 reference sites in the western United States. Classifications were constructed for individual species and for assemblages of sites clustered a priori by similarity on biological characteristics. …

Matching Records In Multiple Databases Using A Hybridization Of Several Technologies., Xiaoyi Wang 1962-

Electronic Theses and Dissertations

A major problem with integrating information from multiple databases is that the same data objects can exist in inconsistent data formats across databases and a variety of attribute variations, making it difficult to identify matching objects using exact string matching. In this research, a variety of models and methods have been developed and tested to alleviate this problem. A major motivation for this research is that the lack of efficient tools for patient record matching still exists for health care providers. This research is focused on the approximate matching of patient records with third party payer databases. This is a …

Improving Liquid State Machines Through Iterative Refinement Of The Reservoir, R David Norton

Theses and Dissertations

Liquid State Machines (LSMs) exploit the power of recurrent spiking neural networks (SNNs) without training the SNN. Instead, a reservoir, or liquid, is randomly created which acts as a filter for a readout function. We develop three methods for iteratively refining a randomly generated liquid to create a more effective one. First, we apply Hebbian learning to LSMs by building the liquid with spike-time dependant plasticity (STDP) synapses. Second, we create an eligibility based reinforcement learning algorithm for synaptic development. Third, we apply principles of Hebbian learning and reinforcement learning to create a new algorithm called separation driven synaptic modification …

Evolution And Field Application Of A Plankton Imaging System, Andrew Walker Remsen

USF Tampa Graduate Theses and Dissertations

Understanding the processes controlling the distribution and abundance of zooplankton has been a primary concern of oceanographers and has driven the development of numerous technologies to more accurately quantify these parameters. This study investigates the potential of a new plankton imaging sensor, the shadowed image particle profiling and evaluation recorder (SIPPER), that I helped develop at the University of South Florida, to address that concern. In the first chapter, results from the SIPPER are compared against concurrently sampling plankton nets and the optical plankton counter (OPC), the most widely used optical zooplankton sampling sensor in the field. It was found …

A Direct Algorithm For The K-Nearest-Neighbor Classifier Via Local Warping Of The Distance Metric, Tohkoon Neo

Theses and Dissertations

The k-nearest neighbor (k-NN) pattern classifier is a simple yet effective learner. However, it has a few drawbacks, one of which is the large model size. There are a number of algorithms that are able to condense the model size of the k-NN classifier at the expense of accuracy. Boosting is therefore desirable for increasing the accuracy of these condensed models. Unfortunately, there does not exist a boosting algorithm that works well with k-NN directly. We present a direct boosting algorithm for the k-NN classifier that creates an ensemble of models with locally modified distance weighting. An empirical study conducted …

Context-Aware Statistical Debugging: From Bug Predictors To Faulty Control Flow Paths, Lingxiao Jiang, Zhendong Su

Research Collection School Of Computing and Information Systems

Effective bug localization is important for realizing automated debugging. One attractive approach is to apply statistical techniques on a collection of evaluation profiles of program properties to help localize bugs. Previous research has proposed various specialized techniques to isolate certain program predicates as bug predictors. However, because many bugs may not be directly associated with these predicates, these techniques are often ineffective in localizing bugs. Relevant control flow paths that may contain bug locations are more informative than stand-alone predicates for discovering and understanding bugs. In this paper, we propose an approach to automatically generate such faulty control flow paths …

Learning Multiple Languages In Groups, Sanjay Jain, Efim Kinber

School of Computer Science & Engineering Faculty Publications

We consider a variant of Gold’s learning paradigm where a learner receives as input different languages (in the form of one text where all input languages are interleaved). Our goal is to explore the situation when a more “coarse” classification of input languages is possible, whereas more refined classification is not. More specifically, we answer the following question: under which conditions, a learner, being fed different languages, can produce grammars covering all input languages, but cannot produce grammars covering input languages for any . We also consider a variant of this task, where each of the output grammars may not …

Heuristic Weighted Voting, Kristine Perry Monteith

Theses and Dissertations

Selecting an effective method for combining the votes of classifiers in an ensemble can have a significant impact on the overall classification accuracy an ensemble is able to achieve. With some methods, the ensemble cannot even achieve as high a classification accuracy as the most accurate individual classifying component. To address this issue, we present the strategy of Heuristic Weighted Voting, a technique that uses heuristics to determine the confidence that a classifier has in its predictions on an instance by instance basis. Using these heuristics to weight the votes in an ensemble results in an overall average increase in …

Limitations And Extensions Of The Wolf-Phc Algorithm, Philip R. Cook

Theses and Dissertations

Policy Hill Climbing (PHC) is a reinforcement learning algorithm that extends Q-learning to learn probabilistic policies for multi-agent games. WoLF-PHC extends PHC with the "win or learn fast" principle. A proof that PHC will diverge in self-play when playing Shapley's game is given, and WoLF-PHC is shown empirically to diverge as well. Various WoLF-PHC based modifications were created, evaluated, and compared in an attempt to obtain convergence to the single shot Nash equilibrium when playing Shapley's game in self-play without using more information than WoLF-PHC uses. Partial Commitment WoLF-PHC (PCWoLF-PHC), which performs best on Shapley's game, is tested on other …

Improving Neural Network Classification Training, Michael Edwin Rimer

Theses and Dissertations

The following work presents a new set of general methods for improving neural network accuracy on classification tasks, grouped under the label of classification-based methods. The central theme of these approaches is to provide problem representations and error functions that more directly improve classification accuracy than conventional learning and error functions. The CB1 algorithm attempts to maximize classification accuracy by selectively backpropagating error only on misclassified training patterns. CB2 incorporates a sliding error threshold to the CB1 algorithm, interpolating between the behavior of CB1 and standard error backpropagation as training progresses in order to avoid prematurely saturated network weights. CB3 …

Solar Activity Detection And Prediction Using Image Processing And Machine Learning Techniques, Gang Fu

Dissertations

The objective of the research in this dissertation is to develop the methods for automatic detection and prediction of solar activities, including prominence eruptions, emerging flux regions and solar flares. Image processing and machine learning techniques are applied in this study. These methods can be used for automatic observation of solar activities and prediction of space weather that may have great influence on the near earth environment.

The research presented in this dissertation covers the following topics: i) automatic detection of prominence eruptions (PBs), ii) automatic detection of emerging flux regions (EFRs), and iii) automatic prediction of solar flares.

In …

Obstacle Avoidance And Path Traversal Using Interactive Machine Learning, Jonathan M. Turner

Theses and Dissertations

Recently there has been a growing interest in using robots in activities that are dangerous or cost prohibitive for humans to do. Such activities include military uses and space exploration. While robotic hardware is often capable of being used in these types of situations, the ability of human operators to control robots in an effective manner is often limited. This deficiency is often related to the control interface of the robot and the level of autonomy that control system affords the human operator. This thesis describes a robot control system, called the safe/unsafe system, which gives a human operator the …

Cognitive And Behavioral Model Ensembles For Autonomous Virtual Characters, Jeffrey S. Whiting

Theses and Dissertations

Cognitive and behavioral models have become popular methods to create autonomous self-animating characters. Creating these models presents the following challenges: (1) Creating a cognitive or behavioral model is a time intensive and complex process that must be done by an expert programmer (2) The models are created to solve a specific problem in a given environment and because of their specific nature cannot be easily reused. Combining existing models together would allow an animator, without the need of a programmer, to create new characters in less time and would be able to leverage each model's strengths to increase the character's …

Active Learning For Part-Of-Speech Tagging: Accelerating Corpus Annotation, George Busby, Marc Carmen, James Carroll, Robbie Haertel, Deryle W. Lonsdale, Peter Mcclanahan, Eric K. Ringger, Kevin Seppi

Faculty Publications

In the construction of a part-of-speech annotated corpus, we are constrained by a fixed budget. A fully annotated corpus is required, but we can afford to label only a subset. We train a Maximum Entropy Markov Model tagger from a labeled subset and automatically tag the remainder. This paper addresses the question of where to focus our manual tagging efforts in order to deliver an annotation of highest quality. In this context, we find that active learning is always helpful. We focus on Query by Uncertainty (QBU) and Query by Committee (QBC) and report on experiments with several baselines and …

Learning To Classify E-Mail, Irena Koprinska, Josiah Poon, James Clark, Jason Yuk Hin Chan

Research Collection School Of Computing and Information Systems

In this paper we study supervised and semi-supervised classification of e-mails. We consider two tasks: filing e-mails into folders and spam e-mail filtering. Firstly, in a supervised learning setting, we investigate the use of random forest for automatic e-mail filing into folders and spam e-mail filtering. We show that random forest is a good choice for these tasks as it runs fast on large and high dimensional databases, is easy to tune and is highly accurate, outperforming popular algorithms such as decision trees, support vector machines and naive Bayes. We introduce a new accurate feature selector with linear time complexity. …

Learning On Complex Simulations, Robert E. Banfield

USF Tampa Graduate Theses and Dissertations

This dissertation explores Machine Learning in the context of computationally intensive simulations. Complex simulations such as those performed at Sandia National Laboratories for the Advanced Strategic Computing program may contain multiple terabytes of data. The amount of data is so large that it is computationally infeasible to transfer between nodes on a supercomputer. In order to create the simulation, data is distributed spatially. For example, if this dissertation was to be broken apart spatially, the binding might be one partition, the first fifty pages another partition, the top three inches of every remaining page another partition, and the remainder confined …

Towards A Self-Calibrating Video Camera Network For Content Analysis And Forensics, Imran Junejo

Electronic Theses and Dissertations

Due to growing security concerns, video surveillance and monitoring has received an immense attention from both federal agencies and private firms. The main concern is that a single camera, even if allowed to rotate or translate, is not sufficient to cover a large area for video surveillance. A more general solution with wide range of applications is to allow the deployed cameras to have a non-overlapping field of view (FoV) and to, if possible, allow these cameras to move freely in 3D space. This thesis addresses the issue of how cameras in such a network can be calibrated and how …

An Analysis Of Misclassification Rates For Decision Trees, Mingyu Zhong

Electronic Theses and Dissertations

The decision tree is a well-known methodology for classification and regression. In this dissertation, we focus on the minimization of the misclassification rate for decision tree classifiers. We derive the necessary equations that provide the optimal tree prediction, the estimated risk of the tree's prediction, and the reliability of the tree's risk estimation. We carry out an extensive analysis of the application of Lidstone's law of succession for the estimation of the class probabilities. In contrast to existing research, we not only compute the expected values of the risks but also calculate the corresponding reliability of the risk (measured by …

Decision Theoretic Agent Design For Personal Rapid Transit Systems, Iheanyi Umez-Eronini, Ferat Sahin

Articles

This paper details a learning decision-theoretic intelligent agent designed to solve the problem of guiding vehicles in the context of Personal Rapid Transit (PRT). The intelligent agents are designed using Bayesian Decision Networks. The agents are designed to utilize the known methods of machine learning with Bayesian Networks (BN): parameter learning and structure learning. In addition, a new method of machine learning with BNs, termed utility learning in this paper, is introduced. BN software for Matlab is used to realize the proposed agent. Additional software is written to simulate the PRT problem using various intelligent agents that utilize one or …