most citedMissing Data: A Comparison of Neural Network and Expectation Maximisation Techniques

27 citations · 99 across the 10 of their papers we have counts for

collaborators

10 papers

cs.AI20083 cited

Relationship between Diversity and Perfomance of Multiple Classifiers for Decision Support

R. Musehane, F. Netshiongolwe, F. V. Nelwamondo +2

The paper presents the investigation and implementation of the relationship between diversity and the performance of multiple classifiers on classification accuracy. The study is c…

cs.LG2008

The use of entropy to measure structural diversity

L. Masisi, V. Nelwamondo, T. Marwala

In this paper entropy based methods are compared and used to measure structural diversity of an ensemble of 21 classifiers. This measure is mostly applied in ecology, whereby speci…

cs.LG200812 cited

The Effect of Structural Diversity of an Ensemble of Classifiers on Classification Accuracy

Lesedi Masisi, Fulufhelo V. Nelwamondo, Tshilidzi Marwala

This paper aims to showcase the measure of structural diversity of an ensemble of 9 classifiers and then map a relationship between this structural diversity and accuracy. The stru…

cs.AI200717 cited

Autoencoder, Principal Component Analysis and Support Vector Regression for Data Imputation

Vukosi N. Marivate, Fulufhelo V. Nelwamodo, Tshilidzi Marwala

Data collection often results in records that have missing values or variables. This investigation compares 3 different data imputation models and identifies their merits by using…

cs.NE20074 cited

Condition Monitoring of HV Bushings in the Presence of Missing Data Using Evolutionary Computing

Sizwe M. Dhlamini*, Fulufhelo V. Nelwamondo**, Tshilidzi Marwala**

The work proposes the application of neural networks with particle swarm optimisation (PSO) and genetic algorithms (GA) to compensate for missing data in classifying high voltage b…

cs.LG20076 cited

HMM Speaker Identification Using Linear and Non-linear Merging Techniques

Unathi Mahola, Fulufhelo V. Nelwamondo, Tshilidzi Marwala

Speaker identification is a powerful, non-invasive and in-expensive biometric technique. The recognition accuracy, however, deteriorates when noise levels affect a specific band of…