Data Augmentation and Deep Neuro-fuzzy Network for Student Performance Prediction with MapReduce Framework

The main aim of an educational institute is to offer high-quality education to students. The system to achieve better quality in the educational system is to find the knowledge from educational data and to discover the attributes that manipulate the performance of students. Student performance prediction is a major issue in education and training, specifically in the educational data mining system. This research presents the student performance prediction approach with the MapReduce framework based on the proposed fractional competitive multi-verse optimization-based deep neuro-fuzzy network. The proposed fractional competitive multi-verse optimization-based deep neuro-fuzzy network is derived by integrating fractional calculus with competitive multi-verse optimization. The MapReduce framework is designed with the mapper and the reducer phase to perform the student performance prediction mechanism with the deep learning classifier. The input data is partitioned at the mapper phase to perform the data transformation process, and thereby the features are selected using the distance measure. The selected unique features are employed for the data segmentation process, and thereafter the prediction strategy is accomplished at the reducer phase by the deep neuro-fuzzy network classifier. The proposed method obtained the performance in terms of mean square error, root mean square error and mean absolute error with the values of 0.338 3, 0.581 7, and 0.391 5, respectively.

Distributed and Weighted Extreme Learning Machine for Imbalanced Big Data Learning

The Extreme Learning Machine（ELM） and its variants are effective in many machine learning applications such as Imbalanced Learning（IL） or Big Data（BD） learning. However, they are unable to solve both imbalanced and large-volume data learning problems. This study addresses the IL problem in BD applications. The Distributed and Weighted ELM（DW-ELM） algorithm is proposed, which is based on the Map Reduce framework. To confirm the feasibility of parallel computation, first, the fact that matrix multiplication operators are decomposable is illustrated.Then, to further improve the computational efficiency, an Improved DW-ELM algorithm（IDW-ELM） is developed using only one Map Reduce job. The successful operations of the proposed DW-ELM and IDW-ELM algorithms are finally validated through experiments.