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In Classification, selection of classifier for data set is difficult. Meta learning automates this process by acquiring knowledge from past experience but generation of meta example is a costly process. So Active Meta Learning can be used which reduces generation of meta examples and at the same time maintaining performance of candidate algorithms. Model presented here provides Ranking using ARR ranking method.

In Classification, Model Selection is one of the critical issues as different models from different categories are available. To select the best model for any given data set is a challenging task. Meta Learning automates this task by acquiring knowledge from the past experience and stores this knowledge into database called Meta Knowledge Base. When new data set comes, stored knowledge can be used for proving ranking of the candidate algorithms. But one of the problems with Meta Learning is generation of Meta Examples as large number of candidate algorithms and data sets are available. To...