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search.filters.applied.f.publicationcategory: Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı × Konu: Data sets ×

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    Univariate margin tree
    (Springer, 2010) Yıldız, Olcay Taner
    In many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we propose a new decision tree learning algorithm called univariate margin tree, where for each continuous attribute, the best split is found using convex optimization. Our simulation results on 47 datasets show that the novel margin tree classifier performs at least as good as C4.5 and LDT with a similar time complexity. For two class datasets it generates smaller trees than C4.5 and LDT without sacrificing from accuracy, and generates significantly more accurate trees than C4.5 and LDT for multiclass datasets with one-vs-rest methodology.
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    Feature extraction from discrete attributes
    (IEEE, 2010) Yıldız, Olcay Taner
    In many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we extract new features by combining k discrete attributes, where for each subset of size k of the attributes, we generate all orderings of values of those attributes exhaustively. We then apply the usual univariate decision tree classifier using these orderings as the new attributes. Our simulation results on 16 datasets from UCI repository [2] show that the novel decision tree classifier performs better than the proper in terms of error rate and tree complexity. The same idea can also be applied to other univariate rule learning algorithms such as C4.5Rules [7] and Ripper [3].
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    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 Taner
    In 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.
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    A novel hybrid electrocardiogram signal compression algorithm with low bit-rate
    (Springer, 2010) Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık Binboğa
    In this paper, a novel hybrid Electrocardiogram (ECG) signal compression algorithm based on the generation process of the Variable-Length Classified Signature and Envelope Vector Sets (VL-CSEVS) is proposed. Assessment results reveal that the proposed algorithm achieves high compression ratios with low level reconstruction error while preserving diagnostic information in the reconstructed ECG signal. The proposed algorithm also slightly outperforms others for the same test dataset.