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...

Multi label classification is a variation of single label classification problem where each instance is associated with more than one class label. The foremost unremarkably used approach to handle multi-label classification problem is to transfer multi-label problem into single label problems, where binary classifier is learned independently for every attainable class labels. However, multi-labeled data generally exhibit relationships between labels, but multi-label classification approach fails to take such relationships under consideration. It's understood that in this type of...

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.