Engineering Commons^™

Learnfca: A Fuzzy Fca And Probability Based Approach For Learning And Classification, Suraj Ketan Samal

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering.

This thesis reviews the state-of-the-art of 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 …

Classification For Mass Spectra And Comprehensive Two-Dimensional Chromatograms, Xue Tian

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Mass spectra contain characteristic information regarding the molecular structure and properties of compounds. The mass spectra of compounds from the same chemically related group are similar. Classification is one of the fundamental methodologies for analyzing mass spectral data. The primary goals of classification are to automatically group compounds based on their mass spectra, to find correlation between the properties of compounds and their mass spectra, and to provide a positive identification of unknown compounds.

This dissertation presents a new algorithm for the classification of mass spectra, the most similar neighbor with a probability-based spectrum similarity measure (MSN-PSSM). Experimental results demonstrate …

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.