Search Results - (( model selection process algorithm ) OR ( java implication based algorithm ))
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1
SURE-Autometrics algorithm for model selection in multiple equations
Published 2016“…This automatic model selection algorithm is better than non-algorithm procedure which requires knowledge and extra time. …”
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Thesis -
2
Algorithmic approaches in model selection of the air passengers flows data
Published 2015“…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.…”
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Conference or Workshop Item -
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Model selection approaches of water quality index data
Published 2016“…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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Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification
Published 2011“…System identification is a method of determining a mathematical relation between variables and terms of a process based on observed input-output data. Model structure selection is one of the important steps in a system identification process. …”
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Optimization of attribute selection model using bio-inspired algorithms
Published 2019“…Attribute selection which is also known as feature selection is an essential process that is relevant to predictive analysis.To date, various feature selection algorithms have been introduced, nevertheless they all work independently. …”
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6
Formulating new enhanced pattern classification algorithms based on ACO-SVM
Published 2013“…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.…”
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Multiple equations model selection algorithm with iterative estimation method
Published 2016“…In particular, an algorithm on model selection for seemingly unrelated regression equations model using iterative feasible generalized least squares estimation method is proposed. …”
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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…The significance of the selected input variable vectors is studied to analyze their effects on the prediction process. …”
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Development of decentralized data fusion algorithm with optimized kalman filter.
Published 2016“…This thesis proposes a data fusion model that facilitates selection of algorithm and recommends selected algorithm to be optimized. …”
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Opposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Method
Published 2024“…In the T-method prediction model, optimization of the model's accuracy is performed through feature selection process by utilizing an orthogonal array. …”
Conference Paper -
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Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925)
Published 2021“…Model selection is the process of choosing a model from a set of possible models. …”
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Monograph -
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Feature Selection with Harmony Search for Classification: A Review
Published 2021“…Feature selection is the process of choosing the most relevant features in a datasets. …”
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Proceeding -
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Nonlinear auto-regressive model structure selection using binary particle swarm optimization algorithm / Ahmad Ihsan Mohd Yassin
Published 2014“…The algorithm searches the solution space by selecting various model structures and evaluating its fitness. …”
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Feature selection algorithms for Malaysian dengue outbreak detection model
Published 2017“…The experimental results showed that the predictive accuracy was improved by applying feature selection process before the predictive modeling process. …”
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An efficient computer forensics selective imaging model
Published 2014“…This paper investigates the impact of the relevant data offsets on the efficiency of the selective imaging process. A practical selective imaging model is presented which includes a digital evidence ordering algorithm (DEOA) for ordering the selected relevant data items. …”
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Book Section -
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Process Sequencing Modeled as TSP with Precedence Constraints - A Genetic Algorithm Approach
Published 2014“…The process sequencing problem can be modeled as the travelling salesman problem with precedence constraints (TSPPC). …”
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VHDL modeling of optimum measurement selection by using genetic algorithm
Published 2009“…A Genetic Algorithm (GA) that is going to be a part of a system for obtaining optimum sensor measurements for a specific process. …”
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Development of classification model between clean water and polluted water based on capacitance properties using Levenberg Marquardt (LM) algorithm of artificial neural network / M...
Published 2020“…There were 1 optimized model selected from the classification process. The accuracy from the selected most optimized models were 100%. …”
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Student Project -
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An integrated algorithm of analytical network process with case-based reasoning to support the selection of an ideal football team formation and players
Published 2021“…The algorithm converts the model to coding, structures the process of entering data, automates the calculations and presents the results in a well-organized and suitable interfaces for the team managerial review purposes. …”
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Thesis