i want to classify persons using their electrical consumption data, imagine the following data set:
"result" is the class variable that I want to detect by using the mean and max Consumptions.
I am sure that it is important for the supervised machine learning algorithm to know whether the data was recorded at the weekend or during the week. Because I were not very happy with the results, I converted the data and now have the following dataset:
The results got worse using SVM and J48-Decision Tree. It makes sense to me why: The column "day" has no correlation to the class "result". Feature selection algorithms would remove the column "day" and a decision tree would realize that the variable "day" has not any correlation to "result".
So my question: Is the first dataset the only option I can use in this case? Are there any classifiers / Feature Selection Algorithms that can deal well with the second data set?
I am using the weka-framwork. If you have any other hints how to get appropiate results, please tell me :) Currently I am using a Feature Selection Algorithm and then run SVM, Decision Tree and k-Nearest-Neighbours on it.
Thanks a lot! Best