WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
Browse
2 results
Search Results
Article Handling Incomplete Data Classification Using Imputed Feature Selected Bagging (IFBAG) Method(Ios Press, 2021-07-09) Khan, Ahmad Jaffar; Raza, Basit; Shahid, Ahmad Raza; Kumar, Yogan Jaya; Faheem, Muhammad; Alquhayz, HaniAlmost all real-world datasets contain missing values. Classification of data with missing values can adversely affect the performance of a classifier if not handled correctly. A common approach used for classification with incomplete data is imputation. Imputation transforms incomplete data with missing values to complete data. Single imputation methods are mostly less accurate than multiple imputation methods which are often computationally much more expensive. This study proposes an imputed feature selected bagging (IFBag) method which uses multiple imputation, feature selection and bagging ensemble learning approach to construct a number of base classifiers to classify new incomplete instances without any need for imputation in testing phase. In bagging ensemble learning approach, data is resampled multiple times with substitution, which can lead to diversity in data thus resulting in more accurate classifiers. The experimental results show the proposed IFBag method is considerably fast and gives 97.26% accuracy for classification with incomplete data as compared to common methods used.Article Citation - WoS: 17Citation - Scopus: 19A Deep Ensemble Approach for Long-Term Traffic Flow Prediction(Springer Heidelberg, 2024-01-27) Cini, Nevin; Aydin, ZaferIn the last 50 years, with the growth of cities and increase in the number of vehicles and mobility, traffic has become troublesome. As a result, traffic flow prediction started to attract attention as an important research area. However, despite the extensive literature, traffic flow prediction still remains as an open research problem, specifically for long-term traffic flow prediction. Compared to the models developed for short-term traffic flow prediction, the number of models developed for long-term traffic flow prediction is very few. Based on this shortcoming, in this study, we focus on long-term traffic flow prediction and propose a novel deep ensemble model (DEM). In order to build this ensemble model, first, we developed a convolutional neural network (CNN), a long short-term memory (LSTM) network and a gated recurrent unit (GRU) network as deep learning models, which formed the base learners. In the next step, we combine the output of these models according to their individual forecasting success. We use another deep learning model to determine the success of the individual models. Our proposed model is a flexible ensemble prediction model that can be updated based on traffic data. To evaluate the performance of the proposed model, we use a publicly available dataset. Experimental results show that the developed DEM model has a mean square error of 0.06 and a mean absolute error of 0.15 for single-step prediction; it shows that achieves a mean square error of 0.25 and a mean absolute error of 0.32 for multi-step prediction. We compared our proposed model with many models in different categories; individual deep learning models (i.e., LSTM, CNN, GRU), selected traditional machine learning models (i.e., linear regression, decision tree regression, k-nearest-neighbors regression) and other ensemble models such as random-forest regression. These results also support the claim that ensemble learning models perform better than individual models.