The "local search" problem - part 1

This week, since we are 2 working on this project, we had to start thinking in a deep and formal way to optimize the already existing metaheuristic ... Oh I forgot, I didn't explain how we got here, so I'm
going to start by first introducing our project.

Supervised classification is a very important task in data mining and a part of the machine learning techniques, it is affecting objects into groups that have the same characteristics based on a set of features or attributes. To simplify things, imagine your data as a table that has a number of columns same as the number of features, let’s call it X, and in addition, you have a number of lines equals the number of your clients, called Y ( when talking about a database in a company for example ), now you’d have a table with a size of ( X x Y ) element at a moment t1.

Now imagine you train your build a model based on this table ( X, Y , t1 ) to try and predict certain future data, but you have a new client who just got into your database, so you need to update your model, now you build a new model based on ( X , Y+1 , t2 ), but hey, 4 more clients came in just moments after you build your model, it becomes (X,Y+5, t3), and while your database grows, the cost of training a new model each time becomes higher after each new model, you should know that the cost to calculate the determinant of a 25x25 matrix is too high, even for a computer, then imagine a 1 00 x 1 000 000 or even more, and this is just one time, and that's what we call the curse of dimensionality.

So people who do research said, since we have no control over new instances of data ( the lines ), let's try and reduce the number of attributes ( the columns ), but the problem with this approach is that ...

Well that would be something for the next article ( a way for me to commit to writing this time since I have that OCD for finishing things I start ).

PS : I wanted to say that, anyone should be proud of what he is/do now, because it is a part of what you will be tomorrow, and for me, this kind of stuff is my mindset now, and after some years, even I won't have the same ideas, that doesn't contradict the fact that I was this way at a certain moment, and it will always be a part of who I am.