Search Results - (( data selection method algorithm ) OR ( variable selection based algorithm ))

Robust variable selection methods for large- scale data in the presence of multicollinearity, autocorrelated errors and outliers by Uraibi, Hassan S.

“…To overcome the instability selection problem, a stability selection approach is put forward to enhance the performance of single-split variable selection method. …”
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Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification by Abd Samad, Md Fahmi

“…System identification is a method of determining a mathematical relation between variables and terms of a process based on observed input-output data. …”
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Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data by Baba, Ishaq Abdullahi

“…Sure screening-based correlation methods are popular tools used to select the most significant variables in the true model in sparse and high dimensional analysis. …”
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Artificial immune system based on real valued negative selection algorithms for anomaly detection by Khairi, Rihab Salah

“…The Real-Valued Negative Selection Algorithms, which are the focal point of this research, generate their detector sets based on the points of self data. …”
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Model selection approaches of water quality index data by Kamarudin, Nur Azulia, Ismail, Suzilah

“…Automatic model selection by using algorithm can avoid huge variability in model specification process compared to manual selection.With the employment of algorithm, the right model selected is then also used for forecasting purposes. …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

“…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
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Extended multiple models selection algorithms based on iterative feasible generalized least squares (IFGLS) and expectation-maximization (EM) algorithm by Nur Azulia, Kamarudin

“…The empirical results for both algorithms performed well as compared to other models selection procedures, particularly using WQI data where the sample size is bigger and has good quality data. …”
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Penalized Quantile Regression Methods And Empirical Mode Decomposition For Improving The Accuracy Of The Model Selection by Ambark, Ali Saleh Al-Massri

“…Therefore, three methods based on a combination of the empirical mode decomposition (EMD) algorithm and penalized quantile regression have been proposed in this study. …”
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Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

“…This study utilizes genetic algorithms based upon the medoid rather than the mean as a centroid-selection schema to improve the clustering efficiency. …”
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Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms by Rachmad, Iqbal, Tri Basuki, Kurniawan, Misinem, ., Edi Surya, Negara, Tata, Sutabri

“…This research proposes a low-cost alternative using intelligent fruit selection systems based on computer vision techniques to address these issues. …”
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A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification by Talpur, N., Abdulkadir, S.J., Hasan, M.H., Alhussian, H., Alwadain, A.

“…To address this issue, various data pre-processing methods called Feature Selection (FS) techniques have been presented in the literature. …”
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Deterministic Mutation Algorithm As A Winner Over Forward Selection Procedure by Md Fahmi, Abd Samad

“…A parsimonious model structure is desirable in enabling easy control design. Two methods of model structure selection are closely looked into and these are deterministic mutation algorithm (DMA) and forward selection procedure (FSP). …”
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Robust correlation feature selection based support vector machine approach for high dimensional datasets by Baba, Ishaq Abdullahi, Mohammed, Mohammed Bappah, Jillahi, Kamal Bakari, Umar, Aliyu, Hendi, Hasan Talib

“…Correlation-based feature selection methods are popular tools used to select the most important variables to include the true model in the analysis of sparse and high-dimensional models. …”
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Feature selection methods for optimizing clinicopathologic input variables in oral cancer prognosis by Chang, S.W., Kareem, S.A., Kallarakkal, A.F., Merican, A.F., Abraham, M.T., Zain, R.B.

“…In this research, we demonstrated the use of feature selection methods to select a subset of variables that is highly predictive of oral cancer prognosis. …”
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Fault diagnostic algorithm for precut fractionation column by Heng, H. Y., Ali, Mohamad Wijayanuddin, Kamsah, Mohd. Zaki

“…The fault diagnostic algorithm is supported by the process history based method and developed by using Borland C++ Builder 6.0. …”
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Sensitivity-based fuzzy multi-objective portfolio model with Value-at-Risk by Zhang, H., Watada, J., Wang, B.

“…Meanwhile, based on the fuzzy simulation technique, the model adapted to a series of different distributed fuzzy variable, an improved particle swarm optimization algorithm (IPSO) is used for numerical simulations. …”
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Effect of input variables selection on energy demand prediction based on intelligent hybrid neural networks by Islam, B., Baharudin, Z., Nallagownden, P.

“…Among the others, the selection of most influential input variables has a critical effect on the forecast results. …”
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Algorithm enhancement for host-based intrusion detection system using discriminant analysis by Dahlan, Dahliyusmanto

“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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Predicting crop yield and field energy output for oil palm using genetic algorithm and neural network models by Hilal, Yousif Yakoub

“…Finally, this research concluded that a genetic algorithm is useful for selecting input variables in oil palm production. …”
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An optimum drill bit selection technique using artificial neural networks and genetic algorithms to increase the rate of penetration by Momeni, M., Hosseini, S.J., Ridha, S., Laruccia, M.B., Liu, X.

“…This paper discusses bit selection by employing a method of combining Artificial Neural Network (ANN) and the computation of Genetic Algorithm (GA). …”
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