6th European Conference - Academic Conferences

6. Conclusions and future work
Suzan Arslanturk et al.
This paper presented a comparison between different attribute selection techniques by using a data
mining tool, Weka. The results show that using different attribute selection techniques help the system by
reducing the curse of dimensionality, time and storage. There is no single attribute selection technique
that gives the best results. The advantages of each technique differs in different situations. One might
give the most accurate results in a certain situation but the storage or time to run the algorithm might take
too long which will effect the efficiency of the algorithm. However, the results show that the Relief and
information gain algorithms give the most accurate results when missing values and noise are added.
This study shows that if in a given dataset noise level is beyond 15% or missing value is beyond 12% the
selected attributes are not reliable even when one uses Relief or information gain algorithms.
The simulation was developed to allow for a particular randomly created matrix to be manipulated with
differing noise, missing value, multicollinearity and combinations of both. Further development of this
simulation model building tool will need to include categorical and continuous data, to allow for more
realistic simulation models.
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