Missing Data Estimation Using Ant-Lion Optimizer Algorithm

2018 ◽  
pp. 103-114
Author(s):  
Collins Achepsah Leke ◽  
Tshilidzi Marwala
Keyword(s):  
Missing Data ◽  
Ant Lion Optimizer ◽  
Ant Lion ◽  
Missing Data Estimation ◽  
Data Estimation

scholarly journals PENDUGAAN DATA HILANG DENGAN METODE YATES DAN ALGORITMA EM PADA RANCANGAN LATTICE SEIMBANG

2015 ◽  
Vol 4 (2) ◽  
pp. 74
Author(s):  
MADE SUSILAWATI ◽  
KARTIKA SARI
Keyword(s):  
Missing Data ◽  
Em Algorithm ◽  
Basic Concept ◽  
Incomplete Data ◽  
Likelihood Function ◽  
Animal Husbandry ◽  
Lattice Design ◽  
The Em Algorithm ◽  
Missing Data Estimation ◽  
Data Estimation

Missing data often occur in agriculture and animal husbandry experiment. The missing data in experimental design makes the information that we get less complete. In this research, the missing data was estimated with Yates method and Expectation Maximization (EM) algorithm. The basic concept of the Yates method is to minimize sum square error (JKG), meanwhile the basic concept of the EM algorithm is to maximize the likelihood function. This research applied Balanced Lattice Design with 9 treatments, 4 replications and 3 group of each repetition. Missing data estimation results showed that the Yates method was better used for two of missing data in the position on a treatment, a column and random, meanwhile the EM algorithm was better used to estimate one of missing data and two of missing data in the position of a group and a replication. The comparison of the result JKG of ANOVA showed that JKG of incomplete data larger than JKG of incomplete data that has been added with estimator of data. This suggest  thatwe need to estimate the missing data.

Committee of Networks for Estimating Missing Data

2010 ◽  
pp. 142-164
Author(s):  
Tshilidzi Marwala
Keyword(s):  
Missing Data ◽  
Radial Basis Functions ◽  
Basis Functions ◽  
Programming Approach ◽  
Support Vector ◽  
Hybrid Particle Swarm Optimization ◽  
Radial Basis ◽  
The Individual ◽  
Missing Data Estimation ◽  
Data Estimation

This chapter introduces a committee of networks for estimating missing data. The first committee of networks consists of multi-layer perceptrons (MLPs), support vector machines (SVMs) and radial basis functions (RBFs). The committee was constructed from a weighted combination of these three networks. The second, third and fourth committees of networks were evolved using a genetic programming approach and used the MLPs, RBFs and SVMs, respectively. The committee of networks was collectively implemented with hybrid particle-swarm optimization and a genetic algorithm for missing data estimation. They were tested on an artificial taster as well as HIV datasets and then compared to the individual multi-layer perceptron, radial basis functions and support vector regression for missing data estimation. It was found that the committee of network approach provided improved results over the three methods acting individually. However, this improvement comes with a higher computational load than does using the individual approaches. Furthermore, it is found that evolving a committee method was a good way of constructing a committee.

Missing Data Estimation Using Rough Sets

2010 ◽  
pp. 94-116
Author(s):  
Tshilidzi Marwala
Keyword(s):  
Missing Data ◽  
Rough Set ◽  
Rough Sets ◽  
Real Data ◽  
Valuable Insight ◽  
Lower And Upper Approximations ◽  
Decision Tables ◽  
Missing Data Estimation ◽  
Data Estimation ◽  
Insight Into

A number of techniques for handling missing data have been presented and implemented. Most of these proposed techniques are unnecessarily complex and, therefore, difficult to use. This chapter investigates a hot-deck data imputation method, based on rough set computations. In this chapter, characteristic relations are introduced that describe incompletely specified decision tables and then these are used for missing data estimation. It has been shown that the basic rough set idea of lower and upper approximations for incompletely specified decision tables may be defined in a variety of different ways. Empirical results obtained using real data are given and they provide a valuable insight into the problem of missing data. Missing data are predicted with an accuracy of up to 99%.

IBMM for Missing Data Estimation

2012 ◽  
pp. 65-75
Author(s):  
Gang Kou ◽  
Daji Ergu ◽  
Yi Peng ◽  
Yong Shi
Keyword(s):  
Missing Data ◽  
Missing Data Estimation ◽  
Data Estimation
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