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For my machine learning study, I tested different algorithms like SVM, SMO, Naive Bayes, Trees etc. All the algorithms resulted with low accuracy levels. In fact the highest accuracy I obtained was 46% using Naive Bayes.

Then I tried to do a feature selection. I used InfoGainAttributeEval in WEKA to do this. It ranked 7 features out of 27 I used, and then, I tried the classification with those 7 features only. But, it resulted with worse accuracy levels. The accuracy of all the algorithms other than SMO got decreased. Naive Bayes resulted with 36% of accuracy.

As I have heard and learned, feature selection is for decrease the complexity and improve the accuracy. But in my case, Why it decreased the accuracy also?

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  • $\begingroup$ "As I have heard and learned, feature selection is for decrease the complexity and improve the accuracy" - This is not necessarily true. While feature selection does decrease complexity, it does NOT have to improve accuracy. Using automatic feature selection is probably not your answer. Try out manual approaches to understand which factors are most strongly related and any domain knowledge to come across any candidate models. However, if your predictors inherently are weakly related to your target, you aren't likely to get a good model. $\endgroup$ – Arun Jose Dec 7 '15 at 7:15
  • $\begingroup$ yes true, my research finding is going to be the features I am considering cannot address my question. BTW can u provide a paper I can refer to mention in my thesis that "feature selection does not have to improve accuracy" $\endgroup$ – vigamage Dec 7 '15 at 8:12

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