Author(s): Rahmad Kurniawan · Mohd Zakree Ahmad Nazri · M. Irsyad · Rado Yendra · Anis Aklima Kamarudin.
Conference: The 5th International Conference on Electrical Engineering and Informatics 2015, At Bali, Indonesia, Volume: 5
Extracting meaningful pattern from data can be challenging. Irrelevant,
redundant, noisy and unreliable data, misinterpretation of results and
incompatibility of a technique to extract unknown patterns from data may
lead analyst to develop an erroneous classifier. This research is
encouraged by ‘No Free Lunch’ theorem that can be simplified as no
classification technique that works best for every problem. This study
tries to make a comparison amongst three main approaches in data mining,
i.e. Decision Tree (DT), Artificial Neural Network (ANN), and Rough Set
Theory (RST). A comparative analysis of the above techniques has been
conducted by using open source's software ROSETTA and WEKA on five
different datasets. The sample sizes are categorized in relation to the
number of attributes and number of instances available in the dataset.
Assessments on the classification model are based on accuracy, amount
and length of the generated rules, error rate and standard deviation.
Based on nine experiments, results show that Artificial Neural Network
provides better accuracy than Decision Tree and Rough Set approach while
Rough Set creates more rules and Decision Tree generate rules faster
than the compared techniques. The results show the trade off of using
different approaches for other researchers in finding the best model for
a particular problem.
Keywords—artificial neural network; decision tree; rough set theory
|Rahmad Kurniawan in The 5th International Conference on Electrical Engineering and Informatics 2015, At Bali, Indonesia, Volume: 5|
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