AN ALL-ATTRIBUTES APPROACH TO SUPERVISED LEARNING

 
 
 
 

AN ALL-ATTRIBUTES APPROACH TO SUPERVISED LEARNING

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dc.contributor.advisor Ralescu, Dr. Anca L. en_US
dc.contributor.author VANCE, DANNY W. en_US
dc.date.accessioned 2008-07-08T15:38:01Z
dc.date.available 2008-07-08T15:38:01Z
dc.date.created 2006 en_US
dc.date.issued 2008-07-08T15:38:01Z
dc.identifier.uri http://rave.ohiolink.edu/etdc/view?acc_num=ucin1162335608 en_US
dc.identifier.uri http://hdl.handle.net/2374.OX/9823
dc.description The objective of supervised learning is to estimate unknowns based on labeled training samples. For example, one may have aerial spectrographic readings for a large field planted in corn. Based on spectrographic observation, one would like to determine whether the plants in part of the field are weeds or corn. Since the unknown to be estimated is categorical or discrete, the problem is one of classification. If the unknown to be estimated is continuous, the problem is one of regression or numerical estimation. For example, one may have samples of ozone levels from certain points in the atmosphere. Based on those samples, one would like to estimate the ozone level at other points in the atmosphere. Algorithms for supervised learning are useful tools in many areas of agriculture, medicine, and engineering, including estimation of proper levels of nutrients for cows, prediction of malignant cancer, document analysis, and speech recognition. A few general references on supervised learning include [1], [2], [3], and [4]. Two recent reviews of the supervised learning literature are [5] and [6]. In general, univariate learning tree algorithms have been particularly successful in classification problems, but they can suffer from several fundamental difficulties, e.g., "a representational limitation of univariate decision trees: the orthogonal splits to the feature's axis of the sample space that univariate tree rely on" [8] and overfit [17]. In this thesis, we present a classification procedure for supervised classification that consists of a new univariate decision tree algorithm (Margin Algorithm) and two other related algorithms (Hyperplane and Box Algorithms). The full algorithm overcomes all of the usual limitations of univariate decision trees and is called the Paired Planes Classification Procedure. The Paired Planes Classification Procedure is compared to Support Vector Machines, K-Nearest Neighbors, and decision trees. The Hyperplane Algorithm allows direct user input as to acceptable error for each class as contrasted with indirect input (through use of a slack variable) with Support Vector Machines. Theoretical and real-life datasets results are shown. Experiments on real-life datasets show that error rates are in some circumstances lower than these supervised learning algorithms, while usually being computationally less expensive by an order of magnitude (or more). en_US
dc.format application/pdf en_US
dc.format 201p. en_US
dc.rights unrestricted en_US
dc.rights Copyright and permissions information available at the source archive en_US
dc.subject machine learning en_US
dc.subject supervised learning en_US
dc.subject support vector machine en_US
dc.subject k-nearest neighbors en_US
dc.subject decision tree en_US
dc.subject SVM en_US
dc.subject KNN en_US
dc.subject CART en_US
dc.subject C4.5 en_US
dc.title AN ALL-ATTRIBUTES APPROACH TO SUPERVISED LEARNING en_US
dc.type Electronic Thesis or Dissertation en_US
dc.degree.name PhD en_US
dc.degree.level doctoral en_US
dc.degree.discipline Engineering : Computer Science & Engineering en_US
dc.degree.grantor University of Cincinnati en_US
dc.contributor.publisher University of Cincinnati / OhioLINK en_US

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