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Yayın Incremental construction of rule ensembles using classifiers produced by different class orderings(IEEE, 2016) Yıldız, Olcay Taner; Ulaş, AydınIn this paper, we discuss a novel approach to incrementally construct a rule ensemble. The approach constructs an ensemble from a dynamically generated set of rule classifiers. Each classifier in this set is trained by using a different class ordering. We investigate criteria including accuracy, ensemble size, and the role of starting point in the search. Fusion is done by averaging. Using 22 data sets, floating search finds small, accurate ensembles in polynomial time.Yayın An incremental model selection algorithm based on cross-validation for finding the architecture of a Hidden Markov model on hand gesture data sets(IEEE, 2009-12-13) Ulaş, Aydın; Yıldız, Olcay TanerIn a multi-parameter learning problem, besides choosing the architecture of the learner, there is the problem of finding the optimal parameters to get maximum performance. When the number of parameters to be tuned increases, it becomes infeasible to try all the parameter sets, hence we need an automatic mechanism to find the optimum parameter setting using computationally feasible algorithms. In this paper, we define the problem of optimizing the architecture of a Hidden Markov Model (HMM) as a state space search and propose the MSUMO (Model Selection Using Multiple Operators) framework that incrementally modifies the structure and checks for improvement using cross-validation. There are five variants that use forward/backward search, single/multiple operators, and depth-first/breadth-first search. On four hand gesture data sets, we compare the performance of MSUMO with the optimal parameter set found by exhaustive search in terms of expected error and computational complexity.
