Search Results - (( variable selection models algorithm ) OR ( java application reoptimize algorithm ))

SURE-Autometrics algorithm for model selection in multiple equations by Norhayati, Yusof

“…This automatic model selection algorithm is better than non-algorithm procedure which requires knowledge and extra time. …”
Get full text
Get full text
Get full text

A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

“…The focus of the paper is to propose a hybrid approach for the selection of the most influential input variables for the training and testing of neural network based hybrid models. …”
Get full text
Get full text

Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition by Yahya, Anwar Ali, Mahmod, Ramlan, Ramli, Abd Rahman

“…In the second stage, the developed variable length genetic algorithm is used to select different sets of lexical cues to constitute the dynamic Bayesian networks' random variables. …”
Get full text
Get full text

Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset.…”
Get full text
Get full text
Get full text

Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition by Ali Yahya, Anwar

“…In the selection phase, a new variable length genetic algorithm is applied to select the lexical cues. …”
Get full text
Get full text

Taguchi?s T-method with Normalization-Based Binary Bat Algorithm by Marlan Z.M., Jamaludin K.R., Harudin N.

Subjects:

Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…Support Vector Machine (SVM) is a present day classification approach originated from statistical approaches.Two main problems that influence the performance of SVM are selecting feature subset and SVM model selection. In order to enhance SVM performance, these problems must be solved simultaneously because error produced from the feature subset selection phase will affect the values of the SVM parameters and resulted in low classification accuracy.Most approaches related with solving SVM model selection problem will discretize the continuous value of SVM parameters which will influence its performance.Incremental Mixed Variable Ant Colony Optimization (IACOMV) has the ability to solve SVM model selection problem without discretising the continuous values and simultaneously solve the two problems.This paper presents an algorithm that integrates IACOMV and SVM.Ten datasets from UCI were used to evaluate the performance of the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with small number of features.…”
Get full text
Get full text
Get full text

Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification by Abd Samad, Md Fahmi

“…A deterministic mutation-based algorithm is introduced to overcome this problem. Identification studies using NARX (Nonlinear AutoRegressive with eXogenous input) models employing simulated systems and real plant data are used to demonstrate that the algorithm is able to detect significant variables and terms faster and to select a simpler model structure than other well-known EC methods.…”
Get full text
Get full text
Get full text

Characteristic wavelength optimization for partial least squares regression using improved flower pollination algorithm by Pauline Ong, Pauline Ong, Jinbao Jian, Jinbao Jian, Jianghua Yin, Jianghua Yin, Guodong Ma, Guodong Ma

“…This study proposes a new wavelength selection method, interval flower pollination algorithm (iFPA), for spectral variable selection in the partial least squares regression (PLSR) model. …”
Get full text
Get full text
Get full text

Development of genetic algorithm for optimization of yield models in oil palm production by Hilal, Yousif Y., Wan Ismail, Wan Ishak, Yahya, Azmi, Ash’aari, Zulfa Hanan

“…Moreover, models were built on the basis of variables that have been selected by the GA. …”
Get full text
Get full text
Get full text

Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925) by Kamarudin, Nur Azulia

“…Model selection is the process of choosing a model from a set of possible models. …”
Get full text
Get full text

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

“…The BAE-Net is found to do a credible job in selecting the correct important variables in the final model.…”
Get full text
Get full text

Assessing the simulation performances of multiple model selection algorithm by Yusof, Norhayati, Ismail, Suzilah, Tuan Muda, Tuan Zalizam

“…The capability of the algorithm in finding the true specification of multiple models is measured by the percentage of simulation outcomes.Overall results show that the algorithm has performed well for a model with two equations.The findings also indicated that the number of variables in the true models affect the algorithm performances. …”
Get full text
Get full text
Get full text

Effect of input variables selection on energy demand prediction based on intelligent hybrid neural networks by Islam, B., Baharudin, Z., Nallagownden, P.

“…In this paper, the important issues related with the best input variable selection for a hybrid model is addressed. …”
Get full text
Get full text

Enhancing Model Selection Based On Penalized Regression Methods And Empirical Mode Decomposition by Al Jawarneh, Abdullah Suleiman Saleh

“…These methods are also utilized to produce a consistent model in terms of variable selection and asymptotically normal estimates and address the multicollinearity problem when it exists between the predictor variables. …”
Get full text
Get full text

Algorithmic approaches in model selection of the air passengers flows data by Ismail, Suzilah, Yusof, Norhayati, Tuan Muda, Tuan Zalizam

“…Algorithm is an important element in any problem solving situation.In statistical modelling strategy, the algorithm provides a step by step process in model building, model testing, choosing the ‘best’ model and even forecasting using the chosen model.Tacit knowledge has contributed to the existence of a huge variability in manual modelling process especially between expert and non-expert modellers.Many algorithms (automated model selection) have been developed to bridge the gap either through single or multiple equation modelling.This study aims to evaluate the forecasting performances of several selected algorithms on air passengers flow data based on Root Mean Square Error (RMSE) and Geometric Root Mean Square Error (GRMSE).The findings show that multiple models selection performed well in one and two step-ahead forecast but was outperformed by single model in three step-ahead forecasts.…”
Get full text
Get full text
Get full text

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. …”
Get full text
Get full text

Mixed variable ant colony optimization technique for feature subset selection and model selection by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

Get full text
Get full text
Get full text

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. …”
Get full text
Get full text
Get full text

Extended multiple models selection algorithms based on iterative feasible generalized least squares (IFGLS) and expectation-maximization (EM) algorithm by Nur Azulia, Kamarudin

“…In conclusion, SURE(IFGLS)-Autometrics and SURE(EM)-Autometrics can be used as models selection algorithms. Additionally, both algorithms are suitable in improving performance of automated models selection procedures. …”
Get full text
Get full text
Get full text
Get full text