Physical Sciences and Mathematics Commons^™

Integrating Information Theory Measures And A Novel Rule-Set-Reduction Tech-Nique To Improve Fuzzy Decision Tree Induction Algorithms, Nael Mohammed Abu-Halaweh

Computer Science Dissertations

Machine learning approaches have been successfully applied to many classification and prediction problems. One of the most popular machine learning approaches is decision trees. A main advantage of decision trees is the clarity of the decision model they produce. The ID3 algorithm proposed by Quinlan forms the basis for many of the decision trees’ application. Trees produced by ID3 are sensitive to small perturbations in training data. To overcome this problem and to handle data uncertainties and spurious precision in data, fuzzy ID3 integrated fuzzy set theory and ideas from fuzzy logic with ID3. Several fuzzy decision trees algorithms and …

Analysis Of Globule Types In Malignant Melanoma, Jin Xu, Kapil Kumar Gupta, William V. Stoecker, Yamini Krishnamurthy, Harold S. Rabinovitz, Austin Bangert, David A. Calcara, Margaret C. Oliviero, Joseph M. Malters, Rhett J. Drugge, R. Joe Stanley, Randy Hays Moss, Mehmed Emre Celebi

Chemistry Faculty Research & Creative Works

Objective: To identify and analyze subtypes of globules based on size, shape, network connectedness, pigmentation, and distribution to determine which globule types and globule distributions are most frequently associated with a diagnosis of malignant melanoma. Design: Retrospective case series of dermoscopy images with globules. Setting: Private dermatology practices. Participants: Patients in dermatology practices. Intervention: Observation only. Main Outcome Measure: Association of globule types with malignant melanoma. Results: The presence of large globules (odds ratio [OR], 5.25) and globules varying in size (4.72) or shape (5.37) had the highest ORs for malignant melanoma among all globule types and combinations studied. Classical …

Towards Google Challenge: Combining Contextual And Social Information For Web Video Categorization, Xiao Wu, Wan-Lei Zhao, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Web video categorization is a fundamental task for web video search. In this paper, we explore the Google challenge from a new perspective by combing contextual and social information under the scenario of social web. The semantic meaning of text (title and tags), video relevance from related videos, and user interest induced from user videos, are integrated to robustly determine the video category. Experiments on YouTube videos demonstrate the effectiveness of the proposed solution. The performance reaches 60% improvement compared to the traditional text based classifiers.

Temporal Data Classification Using Linear Classifiers, Peter Revesz, Thomas Triplet

CSE Conference and Workshop Papers

Data classification is usually based on measurements recorded at the same time. This paper considers temporal data classification where the input is a temporal database that describes measurements over a period of time in history while the predicted class is expected to occur in the future. We describe a new temporal classification method that improves the accuracy of standard classification methods. The benefits of the method are tested on weather forecasting using the meteorological database from the Texas Commission on Environmental Quality.

A Novel Framework For Efficient Automated Singer Identification In Large Music Databases, Jialie Shen, John Shepherd, Bin Cui, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Over the past decade, there has been explosive growth in the availability of multimedia data, particularly image, video, and music. Because of this, content-based music retrieval has attracted attention from the multimedia database and information retrieval communities. Content-based music retrieval requires us to be able to automatically identify particular characteristics of music data. One such characteristic, useful in a range of applications, is the identification of the singer in a musical piece. Unfortunately, existing approaches to this problem suffer from either low accuracy or poor scalability. In this article, we propose a novel scheme, called Hybrid Singer Identifier (HSI), for …

Sequential Bayesian Classification: Dna Barcodes, Michael P. Anderson, Suzanne Dubnicka

Conference on Applied Statistics in Agriculture

DNA barcodes are short strands of nucleotide bases taken from the cytochrome c oxidase subunit 1 (COI) of the mitochondrial DNA (mtDNA). A single barcode may have the form C C G G C A T A G T A G G C A C T G and typically ranges in length from 255 to around 700 nucleotide bases. Unlike nuclear DNA (nDNA), mtDNA remains largely unchanged as it is passed from mother to o spring. It has been proposed that these barcodes may be used as a method of di erentiating between biological species (Hebert, Ratnasingham, and deWaard 2003). …

The Concept Of The Islands Extraction In Satellite Images Using Mathematical Morphology, Przemysław Kupidura

Przemysław Kupidura

The neighborhood is one of the indirect features of the object which may be used in the interpretation of the image. Sometimes, this feature may be essential, what can be shown using an example of islands. According to Oxford Dictionary (Internet edition) island is "a piece of land surrounded by water" so, as we can see, this element of landscape is defined by its neighborhood. The traditional, pixel-based classification is useless in such a case, taking into account only digital numbers of individual pixels. It is impossible to classify islands properly, using this kind of non-contextual algorithms. This paper presents …

Ontology Matching Techniques: A 3-Tier Classification Framework, Nelson K. Y. Leung, Seung Hwan Kang, Sim Kim Lau, Joshua Fan

Faculty of Informatics - Papers (Archive)

No abstract provided.

Clustering, Classification And Explanatory Rules From Harmonic Monitoring Data, Ali Asheibi, David A. Stirling, Danny Sutanto, D A. Robinson

Faculty of Informatics - Papers (Archive)

A method based on the successful AutoClass (Cheeseman & Stutz, 1996) and the Snob research programs (Wallace & Dowe, 1994); (Baxter & Wallace, 1996) has been chosen for our research work on harmonic classification. The method utilizes mixture models (McLachlan, 1992) as a representation of the formulated clusters. This research is principally based on the formation of such mixture models (typically based on Gaussian distributions) through a Minimum Message Length (MML) encoding scheme (Wallace & Boulton, 1968). During the formation of such mixture models the various derivative tools (algorithms) allow for the automated selection of the number of clusters and …

Learning Pattern Classification Tasks With Imbalanced Data Sets, Son Lam Phung, Abdesselam Bouzerdoum, Giang Hoang Nguyen

Faculty of Informatics - Papers (Archive)

This chapter is concerned with the class imbalance problem, which has been recognised as a crucial problem in machine learning and data mining. The problem occurs when there are significantly fewer training instances of one class compared to another class.

The Classification Of Simple Lie Algebras In Maple, D. Russell Sadler

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

Lie algebras are invaluable tools in mathematics and physics as they enable us to study certain geometric objects such as Lie groups and differentiable manifolds. The computer algebra system Maple has several tools in its Lie Algebras package to work with Lie algebras and Lie groups. The purpose of this paper is to supplement the existing software with tools that are essential for the classification of simple Lie algebras over C.

In particular, we use a method to find a Cartan subalgebra of a Lie algebra in polynomial time. From the Cartan subalgebra we can compute the corresponding root system. …

Seasonal Adaptation Of Vegetation Color In Satellite Images For Flight Simulations, Yuzhong Shen, Jiang Li, Vamsi Mantena, Srinivas Jakkula

Electrical & Computer Engineering Faculty Publications

Automatic vegetation identification plays an important role in many applications including remote sensing and high performance flight simulations. This paper proposes a novel method that identifies vegetative areas in satellite images and then alters vegetation color to simulate seasonal changes based on training image pairs. The proposed method first generates a vegetation map for pixels corresponding to vegetative areas, using ISODATA clustering and vegetation classification. The ISODATA algorithm determines the number of clusters automatically. We then apply morphological operations to the clustered images to smooth the boundaries between clusters and to fill holes inside clusters. Six features are then computed …