Combining multiple models – Democracy Versus

When wise people take critical decisions, they usually take into account the opinions from many experts. Now whether to give equal importance to each expert’s opinion or weigh them according to their expertise, their experience is another good logical problem to think on. The main focus of this paper is to closely compare the performance of equally weighted voting (democracy) schemes in Machine learning at different stages against the unequal-weighted voting (meritocracy). Intuitively as we can say that if two different algorithms or methods are expert in different arena of Machine learning then let us choose both for a particular dataset, but depending upon the properties that dataset possess, change the priority of those algorithms in learning. This is the basic concept of unweighted voting. This paper after giving the outline of both the weighted and un-weighted voting compares them on different parameter settings. Introduction