Search Results - (( ensemble construction based algorithm ) OR ( java adaptation optimization algorithm ))

Ant system-based feature set partitioning algorithm for classifier ensemble construction by Abdullah, , Ku-Mahamud, Ku Ruhana

“…In this study, Ant system-based feature set partitioning algorithm for classifier ensemble construction is proposed.The Ant System Algorithm is used to form an optimal feature set partition of the original training set which represents the number of classifiers.Experiments were carried out to construct several homogeneous classifier ensembles using nearest mean classifier, naive Bayes classifier, k-nearest neighbor and linear discriminant analysis as base classifier and majority voting technique as combiner. …”
Get full text
Get full text
Get full text

Ant system-based feature set partitioning algorithm for K-NN and LDA ensembles construction by Abdullah, , Ku-Mahamud, Ku Ruhana

“…Experiments were performed on several University California, Irvine datasets to test the performance of the proposed algorithm.Experimental results showed that the proposed algorithm has successfully constructed better classifier ensemble for k-NN and LDA.…”
Get full text
Get full text
Get full text

A new soft set based pruning algorithm for ensemble method by Mohd Khalid, Awang, Mohd Nordin, Abdul Rahman, Mokhairi, Makhtar

“…Therefore, this paper aims to increase classification accuracy and at the same time minimizing ensemble classifiers by constructing a new ensemble pruning method (SSPM) based on dimensionality reduction in soft set theory. …”
Get full text
Get full text

Pattern generation through feature values modification and decision tree ensemble construction by Akhand, M. A. H, Rahman, M.M. Hafizur, Murase, K.

“…A number of methods have been investigated for constructing ensemble in which some of them train classifiers with the generated patterns. …”
Get full text
Get full text
Get full text

Examining performance of aggregation algorithms for neural network-based electricity demand forecasting by Hassan, S., Khosravi, A., Jaafar, J.

Get full text
Get full text

An ensemble method with cost function on churn prediction by Mohd Khalid, Awang, Mohammad Afendee, Mohamed, Mokhairi, Makhtar

“…The combination of ensemble classifier is calculated based on the simple majority voting algorithm. …”
Get full text
Get full text
Get full text

Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.

An ensemble data summarization approach based on feature transformation to learning relational data by Chung, Seng Kheau

“…A better cluster result can also be produced by combining the cluster results generated from the GA based clustering with Feature Selection and Feature Construction algorithms.…”
Get full text
Get full text
Get full text

Ant system and weighted voting method for multiple classifier systems by Husin, Abdullah, Ku-Mahamud, Ku Ruhana

“…The ant system-based algorithm is used to form the optimal feature set partitions. …”
Get full text
Get full text
Get full text

A Bayesian parameter learning procedure for nonlinear dynamical systems via the ensemble Kalman filter by Ur Rehman, M.J., Dass, S.C., Asirvadam, V.S.

“…Within the parameter learning steps, the MCMC algorithm requires to perform state estimation for which the target distribution is constructed by using the Ensemble Kalman filter (EnKF). …”
Get full text
Get full text

An Improved Ensemble Deep Learning Model Based on CNN for Malicious Website Detection by Do N.Q., Selamat A., Lim K.C., Krejcar O.

An ensemble of neural network and modified grey wolf optimizer for stock prediction by Das, Debashish

“…Additionally, the research restricts the number of variables through feature selection to enhance the performance of the algorithm. Subsequently, the research attempts to construct an ensemble model applying Modified Grey Wolf Optimizer (MGWO) and neural network for stock prediction. …”
Get full text
Get full text

An optimized ensemble for predicting reservoir rock properties in petroleum industry by Kenari, Seyed Ali Jafari

“…The first method isbased on fuzzy genetic algorithm to overcome the premature convergence. The second method is based on two other functions instead of traditional fitness function in genetic algorithmnamely MSE to determine the individual's weight in an ensemble.This approach is based on Huber and Bisquare functions which are meant to avoid the influence of outliers that can be found in many real data such as geosciences data. …”
Get full text
Get full text

An improved multiple classifier combination scheme for pattern classification by Abdullah,

“…A compactness measure is introduced as a parameter in constructing an accurate and diverse classifier ensemble. …”
Get full text
Get full text
Get full text
Get full text

Pruned committee neural network based on accuracy and diversity trade-off for permeability prediction by Kenari, Seyed Ali Jafari, Mashohor, Syamsiah

“…In this paper, first we constructed a committee neural network with different learning algorithms and then proposed an expert pruning method based on diversity and accuracy tradeoff to improve the committee machine framework. …”
Get full text
Get full text
Get full text

Modeling of mechanical properties of silica fume-based green concrete using machine learning techniques by Nafees, A., Amin, M.N., Khan, K., Nazir, K., Ali, M., Javed, M.F., Aslam, F., Musarat, M.A., Vatin, N.I.

Get full text
Get full text

Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

“…The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. …”
Get full text
Get full text
Get full text
Get full text

Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms by Ziyad Sami B.H., Ziyad Sami B.F., Kumar P., Ahmed A.N., Amieghemen G.E., Sherif M.M., El-Shafie A.

“…In this research, machine learning algorithms including regression models, tree regression models, support vector regression (SVR), ensemble regression (ER), and gaussian process regression (GPR) were utilized to predict the compressive and tensile concrete strength. …”