Rauch-Tung-Striebel Based Tucker Feature Selection for Educational Performance Analysis
Keywords:
Data Mining, Rauch-Tung-Striebel Smoother, Feature Selection, Tucker Congruence Cox RegressionAbstract
DM methods have newly obtained an enormous attention in education field. Through immense amount of information in educational dataset, forecasting students’ performance has increased further complexity. Educational DM (EDM) is necessary manner of forecasting academic achievement, blemishing hidden patterns in educational information, improving learning as well as teaching surroundings. Existing techniques were not capable to carry out prognostic analysis through superior accuracy, as well as minimum time utilization owing to their deprived performance in eradicating noise and choosing important features for additional processing. To mention forecast problems to analysis of student performance, design a new ML method called Rauch–Tung–Striebel based Tucker Cox Regressive Feature Selection (RTS-TCRFS). Data pre-processing and feature selection are used to perform educational performance analysis through superior accuracy in minimum time which outcomes in improved forecast of student academic performance. At first, number of student data is gathered as of provided dataset. After that, Rauch–Tung–Striebel Data Pre-processing (RTSDP) process is used for carry out data pre-processing. Data pre-processing is performed to remove noise, as well as miss data values for dimensionality reduction. Now, Rauch-Tung-Striebel fixed-interval smoother create it simple to modernize precedent information through novel observations. RTSDP Smoother is employing forward pass as well as reverse recursion smoother depend on EKF. Once information has processed, Tucker Congruence Cox Regressive Feature Selection is designed to carry out second function of feature selection through enhanced accuracy. Tucker Congruence Coefficient establishes resemblance of extracted features across varied samples to select pertinent features. Therefore, RTS-TCRFS technique improves outcomes of forecast of student academic performance. Experimental assessment is performed with pre-processing time, feature selection accuracy, as well as space complexity. Examined outcomes demonstrate which performance of RTS-TCRFS technique is enhanced compared to existing methods..
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