Machine Learning in Java - Second Edition

Data preprocessing

First, we will utilize Weka's built-in weka.filters.unsupervised.attribute.RemoveUseless filter, which works exactly as its name suggests. It removes the attributes that do not vary much, for instance, all constant attributes are removed. The maximum variance, which is only applied to nominal attributes, is specified with the -M parameter. The default parameter is 99%, which means that if more than 99% of all instances have unique attribute values, the attribute is removed, as follows:

RemoveUseless removeUseless = new RemoveUseless(); 
removeUseless.setOptions(new String[] { "-M", "99" });// threshold 
removeUseless.setInputFormat(data); 
data = Filter.useFilter(data, removeUseless); 

Next, we will replace all of the missing ...