Abstract
There are various data mining techniques to handle with huge amount of data sets. Rough set based classification provides an opportunity in the efficiency of algorithms when dealing with larger datasets. The selection of eligible attributes by using an efficient rule set offers decision makers save time and cost. This paper presents the comparison of the performance of the rough set based algorithms: Johnson’ s, Genetic Algorithm and Dynamic reducts. The performance of algorithms is measured based on accuracy, AUC and standard error for a 3-class classification problem on training on test data sets. Based on the test data, the results showed that genetic algorithm overperformed the others.
Highlights
The rapid development of online platforms or availability of storing data is an emerging area for researchers to form and process the huge amount of data stacks
We evaluate reduction algorithms based on rough set theory for efficient classification with a minimum set of attributes for real estate in Istanbul
After reduction algorithms based on rough set theory, the decision rules obtained as a result of the application of these algorithms are used to determine the classification performance of the algorithms
Summary
The rapid development of online platforms or availability of storing data is an emerging area for researchers to form and process the huge amount of data stacks. Attribute reduction without losing the necessary information from the data set is one of the most capable approaches used for this purpose is offered by the Rough Set Theory [2]. The reduct generation or approximations to reduction generation in rough set theory was studied by many researchers In this regards, Johnson (1974) provided a possible classification of optimization problems as to the behaviour of their approximation algorithms [3]. Experimental results proved competitive performance for FSA-based approach showing that FSA combined with rough sets forms a useful technique for the attribute reduction problem. We evaluate reduction algorithms based on rough set theory for efficient classification with a minimum set of attributes for real estate in Istanbul.
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