Feature Selection for Ranking using Heuristics based Learning to Rank using Machine Learning
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Abstract
Machine Learning based ranking is done every filed. Ranking is also solved by using (LTR i. e. learning to Rank)
techniques. In this work, we propose a Heuristics LTR based models for information retrieval. Different new
algorithms are tackling the problem feature selection in ranking. In this proposed model try to makes use of the
simulated annealing and Principal Component analysis for document retrieval using learning to rank. A use of
simulated annealing heuristics method used for the feature Selection to test the results improvement. The feature
extraction technique helps to find the minimal subsets of features for better results. The core idea of the proposed
framework is to make use of k-fold cross validation of training queries in the SA as well as the training queries
in the any feature selection method to extract features and only using training quires make use of validation
and test quires to create a learning model with LTR. The standard evaluation measures are used to verify the
significant improvement in the proposed model. Performance of proposed model are measured based on prediction
on some selected benchmark datasets, Improvement in the results are compare on recent high performed pairwise
algorithms.
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