Abstract:
The main focus of this thesis is to evaluate and compare Hyperbalilearning
algorithm (HBL) to other learning algorithms. In this work HBL is
compared to feed forward artificial neural networks using back propagation
learning, K-nearest neighbor and 103 algorithms.
In order to evaluate the similarity of these algorithms, we carried out
three experiments using nine benchmark data sets from UCI machine
learning repository. The first experiment compares HBL to other algorithms
when sample size of dataset is changing. The second experiment
compares HBL to other algorithms when dimensionality of data changes.
The last experiment compares HBL to other algorithms according to the
level of agreement to data target values.
Our observations in general showed, considering classification accuracy
as a measure, HBL is performing as good as most ANn variants.
Additionally, we also deduced that HBL.:s classification accuracy outperforms
103's and K-nearest neighbour's for the selected data sets.
