Reference no: EM133520940
Case Study: For this question you need to implement three multi-classifiers to identify attack and normal behaviour from the UNSW-NB15 intrusion dataset. You are required to read the data from training set (175,341 records) and test set (82,332 records).
You are required to implement it by using the publicly available machine learning software WEKA.
For this task you will need two files available on below link:
• training.arff and test.arff.
You need to perform the following steps:
• Import training data.
• For each classifier:
- Select an appropriate classifier (do not choose any meta classifier)
- Specify test option
- Perform the training
- Supply test data set
- Evaluate the classifier.
Question: You need to repeat for at least 3 classifiers (Random Forest, Random Tree, J48, and eventually select the results from the best 2 classifiers.
You need to include in your report the following:
1. Screenshot of the performance details for 3 classifiers
2. Compare the results of the selected best 2 classifiers, evaluating with the metrics: Accuracy, precision, recall, F1-Score and false positive rate.
3. Which classifier gave the best performance overall? Justify your selection of the 'best' classifier and given reasons why you think it is the best. Is there any way to improve the performance further?
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