Browsing by Subject "Over fitting problem"
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Item Open Access Energy consumption forecasting via order preserving pattern matching(IEEE, 2014-12) Vanlı, N. Denizcan; Sayın, Muhammed O.; Yıldız, Hikmet; Göze, Tolga; Kozat, Süleyman S.We study sequential prediction of energy consumption of actual users under a generic loss/utility function. Particularly, we try to determine whether the energy usage of the consumer will increase or decrease in the future, which can be subsequently used to optimize energy consumption. To this end, we use the energy consumption history of the users and define finite state (FS) predictors according to the relative ordering patterns of these past observations. In order to alleviate the overfitting problems, we generate equivalence classes by tying several states in a nested manner. Using the resulting equivalence classes, we obtain a doubly exponential number of different FS predictors, one among which achieves the smallest accumulated loss, hence is optimal for the prediction task. We then introduce an algorithm to achieve the performance of this FS predictor among all doubly exponential number of FS predictors with a significantly reduced computational complexity. Our approach is generic in the sense that different tying configurations and loss functions can be incorporated into our framework in a straightforward manner. We illustrate the merits of the proposed algorithm using the real life energy usage data. © 2014 IEEE.Item Open Access Novelty detection using soft partitioning and hierarchical models(IEEE, 2017) Ergen, Tolga; Gökçesu, Kaan; Şimşek, Mustafa; Kozat, Süleyman SerdarIn this paper, we study novelty detection problem and introduce an online algorithm. The algorithm sequentially receives an observation, generates a decision and then updates its parameters. In the first step, to model the underlying distribution, algorithm constructs a score function. In the second step, this score function is used to make the final decision for the observed data. After thresholding procedure is applied, the final decision is made. We obtain the score using versatile and adaptive nested decision tree. We employ nested soft decision trees to partition the observation space in an hierarchical manner. Based on the sequential performance, we optimize all the components of the tree structure in an adaptive manner. Although this in time adaptation provides powerful modeling abilities, it might suffer from overfitting. To circumvent overfitting problem, we employ the intermediate nodes of tree in order to generate subtrees and we then combine them in an adaptive manner. The experiments illustrate that the introduced algorithm significantly outperforms the state of the art methods.