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M.S. Thesis Defense: Suraj Ketan Samal

11:00 am – 12:00 pm
Zoom Room:
“LearnFCA: A Fuzzy FCA and Probability Based Approach for Learning and Classification”

This thesis reviews the state-of-the-art theory of Formal Concept Analysis (FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of its applications and various approaches adopted by researchers in the areas of data analysis, knowledge management with emphasis on data-learning and classification problems. We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabeled features. We evaluate LearnFCA on encodings from three datasets - MNIST, OMNIGLOT and cancer images with interesting results and varying degrees of success.

Committee Members:
Dr. Jitender Deogun (Advisor)
Dr. Vinodchandran N. Variyam
Dr. Mohammad Rashedul Hasan

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