Razvoj metode za izbiro klasifikatorja - Černezel, Aleš (visokošolska dela, 2016)
In this dissertation we present the development of a classifier selection method. The main contribution of the method is to obtain the most appropriate combinations of: method for measuring accuracy, classification algorithm, and size of the training set --- all in accordance with user-defined criteria. The method is general and therefore adjustable and expandable. The method's procedure is formally defined in the form of pseudo-code. For the purpose of providing theoretical background, several experiments were conducted and their results were analysed with a series of statistical tests. Results of the research yielded the following contributions to science. Formalising decisions and criteria for choosing the most appropriate method for measuring accuracy. Formalising decisions and criteria for choosing the most appropriate classification algorithm. Selecting a best-fit learning curve model. Formalising terminal criteria for selecting the most appropriate train set size.
Keywords: algorithm comparison; approximation; aproksimacija krivulj; classification algorithm; cross-validation; Disertacije; doktorske disertacije; eksponentni zakon; Izbira; klasifikacijski algoritmi; Klasifikatorji; machine learning; navzkrižna validacija; podatkovne zbirke; primerjava algoritmov; Strojno učenje; terminal criteria