A novel feature selection algorithm using ACO-Ant Colony Optimization, to extract feature words from a given web page and then to generate an optimal feature set based on ACO Meta heuristics and normalized weight defined as a learning function of their learned weights, position and frequency of feature in the web page.
Experimental results: using NBC- 0.853, 0.788, 0.814, 0.937 using SVM 0.760, 0.873, 0.807, 0.936 (in the following order IR-precision, IR-Recall, F-Measure, Area under precision-recall curve (AUC)).

To ascertain the validity of the proposed measure, we performed the experiments of web document categorization and the obtained results using the proposed measure were compared with those using other commonly used measures.

Webkb datasets(CMU Machine Learning Repository) were adopted in our simulation experiments. Dataset along with noise were firstly fed into feature selectors, which will generate feature subsets from the datasets. After that, Newly selected features were passed to external learning algorithms to assess classification performance. NBC (Naive Bayes Classifier) and (SVM) Support Vector Machine classifier, were chosen to test prediction capability of the selected subset. All test were done on experimental platform Weka. To achieve impartial results, 10-fold cross validation were performed on the datasets using both the classifier.This is to say, for each datasets, classification algorithm was run on 10 times and at each time, a 10-fold cross validation was used, and the final results were their average values.