Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Validating the ISO/IEC 15504 Measure of Software Requirements Analysis Process Capability
IEEE Transactions on Software Engineering
Software Cost Estimation with Incomplete Data
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
Dealing with Missing Software Project Data
METRICS '03 Proceedings of the 9th International Symposium on Software Metrics
A Grey-Based Nearest Neighbor Approach for Missing Attribute Value Prediction
Applied Intelligence
Data envelopment analysis with missing values: an interval DEA approach
Applied Mathematics and Computation
Mining itemsets in the presence of missing values
Proceedings of the 2007 ACM symposium on Applied computing
Combined association rules for dealing with missing values
Journal of Information Science
Sequential imputation for missing values
Computational Biology and Chemistry
Handling Missing Values when Applying Classification Models
The Journal of Machine Learning Research
A consistency-based procedure to estimate missing pairwise preference values
International Journal of Intelligent Systems
Ameliorative missing value imputation for robust biological knowledge inference
Journal of Biomedical Informatics
Impact of imputation of missing values on classification error for discrete data
Pattern Recognition
Sequential local least squares imputation estimating missing value of microarray data
Computers in Biology and Medicine
POP algorithm: Kernel-based imputation to treat missing values in knowledge discovery from databases
Expert Systems with Applications: An International Journal
Imputation of Missing Data Using PCA, Neuro-Fuzzy and Genetic Algorithms
Advances in Neuro-Information Processing
Analysis of longitudinal data with intermittent missing values using the stochastic EM algorithm
Computational Statistics & Data Analysis
Cost-sensitive classification with respect to waiting cost
Knowledge-Based Systems
Feature interval learning algorithms for classification
Knowledge-Based Systems
Missing data imputation in multivariate data by evolutionary algorithms
Computers in Human Behavior
Inductive learning models with missing values
Mathematical and Computer Modelling: An International Journal
Hi-index | 0.00 |
Missing data are a part of almost all research, and we all have to decide how to deal with it from time to time. There are a number of alternative ways of dealing with missing data. The problem of handling missing data has been treated adequately in various real world data sets. Several statistical methods have been developed since the early 1970s, when the manipulation of complicated numerical calculations became feasible with the advancement of computers. The purpose of this research is to estimate missing values by using genetic algorithm (GA) approach in a randomized complete block design (RCBD) table and to compare the computational results with three other methods, namely, particle swarm optimization (PSO), Artificial Neural Network (ANN), approximate analysis and exact regression method. Furthermore, 30 independent experiments were conducted to estimate missing values in 30 RCBD tables by GA, PSO, ANN, exact regression and approximate analysis methods. Computational results indicated that the best answer (in the last 10-chromosome population) obtained by GA is frequently the same as the missing value, with the mean value being close to the missing observation. It is concluded that GA provides much better estimation than the other methods. The superiority of GA is shown through lower error estimations and also Pearson correlation experiment. Therefore, it is suggested to utilize GA approach of this study for estimating missing values for RCBD.