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This work presents a novel approach by considering teaching learning based optimization (TLBO) and radial basis function neural networks (RBFNs) for building a classifier for the databases with missing values and irrelevant features. The least square estimator and relief algorithm have been used for imputing the database and evaluating the relevance of features, respectively. The preprocessed dataset is used for developing a classifier based on TLBO trained RBFNs for generating a concise and meaningful description for each class that can be used to classify subsequent instances with no known class label. The method is evaluated extensively through a few bench-mark datasets obtained from UCI repository. The experimental results confirm that our approach can be a promising tool towards constructing a classifier from the databases with missing values and irrelevant attributes.

The 12^th South Asian Games were held in India in Guawhati and Shillong in February 2016, after repeated rescheduling. There were a number of challenges to organizing the games such as lack of infrastructure, legacy of corruption from past games, shortage of time etc. However, the games were held within 90 days of the announcement of venues and final dates. Sri Yadav, the Secretary, Department of Sports wants to understand the key drivers of success behind organizing the event. He also wants to understand how the success of Indian athletes at SAG could be leveraged for success at larger events.