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Distance Learning and Attribute Importance Analysis by Linear Regression on Idealized Distance Functions

Singh, Rupesh Kumar
http://orcid.org/0000-0002-4846-5785
http://rave.ohiolink.edu/etdc/view?acc_num=wright1495909607902884

Abstract Details

2017, Master of Science in Computer Engineering (MSCE), Wright State University, Computer Engineering.
A good distance metric is instrumental on the performance of many tasks including classification and data retrieval. However, designing an optimal distance function is very challenging, especially when the data has high dimensions.Recently, a number of algorithms have been proposed to learn an optimal distance function in a supervised manner, using data with class labels. In this thesis we proposed methods to learn an optimal distance function that can also indicate the importance of attributes. Specifically, we present several ways to define idealized distance functions, two of which involving distance error correction involving KNN classification, and another involving a two-constant defined distance function. Then we use multiple linear regression to produce regression formulas to represent the idealized distance functions. Experiments indicate that distances produced by our approaches have classification accuracy that are fairly comparable to existing methods. Importantly, our methods have added bonus of using weights on attributes to indicate the importance of attributes in the constructed optimal distance functions. Finally, the thesis presents importance of attributes on a number of datasetsfrom the UCI repository.
Guozhu Dong, Ph.D. (Advisor)
Keke Chen, Ph.D. (Committee Member)
Michelle Cheatham, Ph.D. (Committee Member)
44 p.
Computer Engineering; Computer Science
Distance learning; move bad neighbors out; global class gap; two-constant distance; weighted distance function; Euclidean; Manhattan

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Citations

  • Singh, R. K. (2017). Distance Learning and Attribute Importance Analysis by Linear Regression on Idealized Distance Functions [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1495909607902884

    APA Style (7th edition)

  • Singh, Rupesh. Distance Learning and Attribute Importance Analysis by Linear Regression on Idealized Distance Functions . 2017. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1495909607902884.

    MLA Style (8th edition)

  • Singh, Rupesh. "Distance Learning and Attribute Importance Analysis by Linear Regression on Idealized Distance Functions ." Master's thesis, Wright State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1495909607902884

    Chicago Manual of Style (17th edition)

wright1495909607902884
763
© 2017, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.