Learning in the real world environment: a classification model based on sensitivity to within-dimension and between-category variation of feature frequencies
Date
2018-06-22
Authors
Lam, Newman Ming Ki
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Abstract
Research on machine learning has taken numerous different
directions. The present study focussed on the microstructural
characteristics of learning systems. It was
postulated that learning systems consist of a macrostructure
which controls the flow of information, and a
micro-structure which manipulates information for decision
making. A review of the literature suggested that the basic
function of the micro-structure of learning systems was to
make a choice among a set of alternatives. This decision
function was then equated with the task of making
classification decisions. On the basis of the requirements
for practical learning systems, the feature frequency
approach was chosen for model development. An analysis of
the feature frequency approach indicated that an effective
model must be sensitive to both within-dimension and
between-category variations in frequencies. A model was
then developed to provide for such sensitivities. The model
was based on the Bayes' Theorem with an assumption of
uniform prior probability of occurrence for the categories.
This model was tested using data collected for
neuropsychological diagnosis of children. Results of the
tests showed that the model was capable of learning and
provided a satisfactory level of performance. The
performance of the model was compared with that of other
models designed for the same purpose. The other models
included NEXSYS, a rule-based system specially design for
this type of diagnosis, discriminant analysis, which is a
statistical technique widely used for pattern recognition,
and neural networks, which attempt to simulate the neural
activities of the brain. Results of the tests showed that
the model's performance was comparable to that of the other
models. Further analysis indicated that the model has certain advantages in that it has a simple structure, is
capable of explaining its decisions, and is more efficient
than the other models.
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Keywords
Machine learning