A Logical Approach for Empirical Risk Minimization in Machine Learning for Data Stratification
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Date
2017
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Research Journal of Mathematics and Computer Science
Abstract
The data-driven methods capable of understanding, mimicking
and aiding the information processing tasks of Machine Learning (ML) have been applied in an increasing range over the past
years in diverse areas at a very high rate, and had achieved
great success in predicting and stratifying given data instances of a problem domain. There has been generalization on the
performance of the classifier to be the optimal based on the
existing performance benchmarks such as accuracy, speed,
time to learn, number of features, comprehensibility, robustness,
scalability and interpretability. However, these benchmarks alone
do not guarantee the successful adoption of an algorithm for
prediction and stratification since there may be an incurring risk
in its adoption. Therefore, this paper aims at developing a logical
approach for using Empirical Risk Minimization (ERM) technique
to determine the machine learning classifier with the minimum
risk function for data stratification. The generalization on the
performance of optimal algorithm was tested on BayesNet, Multilayered perceptron, Projective Adaptive Resonance Theory
(PART) and Logistic Model Trees algorithms based on existing
performance benchmarks such as correctly classified instances,
time to build, kappa statistics, sensitivity and specificity to determine the algorithms with great performances. The study showed
that PART and Logistic Model Trees algorithms perform well
than others. Hence, a logical approach to apply Empirical Risk
Minimization technique on PART and Logistic Model Trees algorithms is shown to give a detailed procedure of determining their
empirical risk function to aid the decision of choosing an algorithm to be the best fit classifier for data stratification. This therefore serves as a benchmark for selecting an optimal algorithm
for stratification and prediction alongside other benchmarks.
Description
Keywords
Classification Algorithm, Data Stratification, Machine Learning, Empirical Risk Minimization, Supervised Learning
Citation
Taiwo et al.,. A Logical Approach for Empirical Risk Minimization in Machine Learning for Data Stratification. Research Journal of Mathematics and Computer Science, 2017; 1:3