Adaptive Learning for Algorithm Selection in Classification

No learner is generally better than another learner. If a learner performs better than another learner on some learning situations, then the first learner usually performs worse than the second learner on other situations. In other words, no single learning algorithm can perform well and uniformly outperform other algorithms over all learning or data mining tasks. There is an increasing number of algorithms and practices that can be used for the very same application. With the explosion of available learning algorithms, a method for helping user selecting the most appropriate algorithm or combination of algorithms to solve a problem is becoming increasingly important. In this paper we are using meta-learning to relate the performance of machine learning algorithms on the different datasets. The paper concludes by proposing the system which can learn dynamically as per the given data.