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Full-Text Articles in Engineering
An Online Approach For Feature Selection For Classification In Big Data, Nasrin Banu Nazar, Radha Senthilkumar
An Online Approach For Feature Selection For Classification In Big Data, Nasrin Banu Nazar, Radha Senthilkumar
Turkish Journal of Electrical Engineering and Computer Sciences
Feature selection (FS), also known as attribute selection, is a process of selection of a subset of relevant features used in model construction. This process or method improves the classification accuracy by removing irrelevant and noisy features. FS is implemented using either batch learning or online learning. Currently, the FS methods are executed in batch learning. Nevertheless, these techniques take longer execution time and require larger storage space to process the entire dataset. Due to the lack of scalability, the batch learning process cannot be used for large data. In the present study, a scalable efficient Online Feature Selection (OFS) …
A Fast Feature Selection Approach Based On Extreme Learning Machine And Coefficient Of Variation, Ömer Faruk Ertuğrul, Mehmet Emi̇n Tağluk
A Fast Feature Selection Approach Based On Extreme Learning Machine And Coefficient Of Variation, Ömer Faruk Ertuğrul, Mehmet Emi̇n Tağluk
Turkish Journal of Electrical Engineering and Computer Sciences
Feature selection is the method of reducing the size of data without degrading their accuracy. In this study, we propose a novel feature selection approach, based on extreme learning machines (ELMs) and the coefficient of variation (CV). In the proposed approach, the most relevant features are identified by ranking each feature with the coefficient obtained through ELM divided by CV. The achieved accuracies and computational costs, obtained with the use of features selected via the proposed approach in 9 classification and 26 regression benchmark data sets, were compared to those obtained with all features, as well as those obtained with …
Stock Daily Return Prediction Using Expanded Features And Feature Selection, Hakan Gündüz, Zehra Çataltepe, Yusuf Yaslan
Stock Daily Return Prediction Using Expanded Features And Feature Selection, Hakan Gündüz, Zehra Çataltepe, Yusuf Yaslan
Turkish Journal of Electrical Engineering and Computer Sciences
Stock market prediction is a very noisy problem and the use of any additional information to increase accuracy is necessary. In this paper, for the stock daily return prediction problem, the set of features is expanded to include indicators not only for the stock to be predicted itself but also a set of other stocks and currencies. Afterwards, different feature selection and classification methods are utilized for prediction. The daily close returns of the 3 most traded stocks (GARAN, THYAO, and ISCTR) in Borsa İstanbul (BIST) are predicted using indicators computed on those stocks, indicators for all the other stocks …