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    Generalized FLIC: Learning with Misclassification for Binary Classifiers

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    CHOUDHURY_ku_0099M_13736_DATA_1.pdf (3.971Mb)
    Issue Date
    2014-12-31
    Author
    Choudhury Arunabha
    Publisher
    University of Kansas
    Format
    71 pages
    Type
    Thesis
    Degree Level
    M.S.
    Discipline
    Electrical Engineering & Computer Science
    Rights
    Copyright held by the author.
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    Abstract
    This work formally introduces a generalized fuzzy logic and interval clustering (FLIC) technique which, when integrated with existing supervised learning algorithms, improves their performance. FLIC is a method that was first integrated with neural network in order to improve neural network's performance in drug discovery using high throughput screening (HTS). This research strictly focuses on binary classification problems and generalizes the FLIC in order to incorporate it with other machine learning algorithms. In most binary classification problems, the class boundary is not linear. This pose a major problem when the number of outliers are significantly high, degrading the performance of the supervised learning function. FLIC identifies these misclassifications before the training set is introduced to the learning algorithm. This allows the supervised learning algorithm to learn more efficiently since it is now aware of those misclassifications. Although the proposed method performs well with most binary classification problems, it does significantly well for data set with high class asymmetry. The proposed method has been tested on four well known data sets of which three are from UCI Machine Learning repository and one from BigML. Tests have been conducted with three well known supervised learning techniques: Decision Tree, Logistic Regression and Naive Bayes. The results from the experiments show significant improvement in performance. The paper begins with a formal introduction to the core idea this research is based upon. It then discusses a list of other methods that have either inspired this research or have been referred to, in order to formalize the techniques. Subsequent sections discuss the methodology and the algorithm which is followed by results and conclusion.
    URI
    http://hdl.handle.net/1808/18418
    Collections
    • Engineering Dissertations and Theses [1055]
    • Theses [3828]

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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