Dynamic-Ranking-of-Classification-Algorithms

There are a plethora of algorithms in data mining, machine learning and pattern recognition areas. It is very difficult for non-experts to select a particular algorithm. Hence, according to current application or task at hand, recommendation of appropriate classification algorithm for given new dataset is a very important and useful task. According to NO-FREE-LUNCH theorem, there is no best classifier for different classification problems. It is difficult to predict which learning algorithm will work best for a particular type of data and domain. A meta-learning method is presented to support selection of candidate learning algorithms. Meta learning tries to address the problem of algorithm selection by recommending promising classifiers based on meta-features. It uses a k-Nearest Neighbor algorithm to identify the datasets that are most similar to the one at hand. The performance of the candidate algorithms on those datasets is used to generate a recommendation to the user in the form of a ranking. The performance is assessed using a multi-criteria evaluation measure that takes accuracy, precision, recall and execution time into account. The evaluation methodology is general and can be adapted to other ranking problems.

Team Members:

Rohan Kasture
Anushka Kher
Supriya Shetty

Datasets taken from UCI repositories