Educational Data Mining: Prediction of Students Performance or Related Area Use of K- Star Algorithm or Ensemble Method
Year: 2018
Type: article
Abstract: The principle objective about higher training foundations is on furnish nature training to its learners. Restricted should accomplish most elevated level for caliber over higher training framework will be finding learning to prediction in regards to enrolment from claiming people done a specific course, distance about conventional classroom educating support model, identification of unreasonable methods utilized within on the web examination, identification of abnormal values in the bring about shortages sheets of the students, prediction something like students’ execution et cetera. The information is stowed away "around those instructive information set Also it may be extractable through information mining strategies. This paper employs sound signal for condition monitoring of roller bearing by K-star classifier and k-nearest neighborhood classifier. The statistical feature extraction is performed from acquired sound signals. Then two layer feature selection is done using J48 decision tree algorithm and random tree algorithm. These selected features were classified using K-star classifier and k-nearest neighborhood classifier and parametric optimization is performed to achieve the maximum classification accuracy. The classification results for both K-star classifier and k-nearest neighborhood classifier for condition monitoring of roller bearing using sound signals were compared. Keywords:- Educational Data Mining (EDM); Classification; Knowledge Discovery in Database (KDD); ID Algorithm K-star, k-nearest neighborhood; k-NN, machine learning approach, condition monitoring, fault diagnosis, roller bearing, decision tree algorithm; J48, random tree algorithm, decision making, two layer feature selection, sound signal, statistical features.
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W7216739344