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\begin{document}

\title{Rough Set Theory and Association Rule Mining for Detecting Interactions
and Improving Machine Learning Predictions for Weather Prediction in
Kenya.}



\author[1]{Isaac Kega}%
\author[2]{Lawrence  Nderu}%
\author[2]{Ronald Mwangi}%
\author[2]{Dennis Njagi}%
\affil[1]{Zetech University}%
\affil[2]{Jomo Kenyatta University of Agriculture and Technology}%


\vspace{-1em}



  
  \date{June 16, 2024}


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\begin{abstract}
Recently, the ever-increasing complexity of datasets has necessitated
the development of sophisticated techniques to uncover meaningful
patterns and interactions within the data. This paper investigates the
synergy between Rough Set Theory and Association Rule Mining, which is a
potent approach to detecting interactions and enhancing the prediction
capabilities of machine learning models. The proposed framework
leverages the Greedy Heuristic Method for reduct generation, an
established technique in Rough Set Theory, to efficiently identify
relevant features and reduce the dimensionality of the dataset.
Furthermore, Association Rule Mining extracts association rules from the
data, revealing interesting relationships and dependencies among the
features. These association rules are transformed into binary values,
representing the detected interactions, to create a concise yet
informative representation of the data's intrinsic relationships. This
binary representation is ideal for integration into machine learning
models, enabling them to exploit the discovered interactions and gain a
more comprehensive understanding of the underlying patterns. To assess
the effectiveness of our proposed framework, we propose a comprehensive
experiment involving a weather dataset scraped from www.wunderground.com
for Kariki farm in the Juja sub-county, Kiambu County, Kenya. Using
detected interactions, we modelled them to base machine learning models,
including Naive Bayes, Decision Trees, Support Vector Machines (SVM),
and Logistic Regression models. We compared the performance of these
models while using the detected interactions versus not using the
detected interactions. Through extensive experimentation, we demonstrate
that our proposed approach is more effective than traditional machine
learning models without interaction detection. Our results indicate that
our interaction detection method framework significantly improves the
prediction accuracy of the tested models on the benchmark datasets. This
enhancement in accuracy highlights the practical relevance and potential
benefits of adopting our approach to uncover valuable insights from
datasets.%
\end{abstract}%



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