计算机科学
人工智能
特征选择
机器学习
朴素贝叶斯分类器
随机森林
支持向量机
决策树
Python(编程语言)
聚类分析
特征(语言学)
阿达布思
感知器
模式识别(心理学)
数据挖掘
人工神经网络
操作系统
哲学
语言学
作者
Ravinder Ahuja,S. C. Sharma
标识
DOI:10.6688/jise.202109_37(5).0001
摘要
Machine learning has emerged as the most important and widely used tool in resolving the administrative and other educational related problems. Most of the research in the educational field centers on demonstrating the student's potential rather than focusing on faculty quality. In this paper the performance of the instructor is evaluated through feedback collected from students in the questionnaire form. The unlabelled dataset is taken from UCI machine learning repository consisting of 5820 records with 33 attributes. Firstly, the dataset is labelled(three labels) using agglomerative clustering and the k-means algorithms. Further, five feature selection techniques (Random Forest,Principal Component Analysis, Recursive Feature Selection, Univariate Feature Selection, and Genetic Algorithm) are applied to extract essential features. After feature selection, twelve classification algorithms (K Nearest Neighbor, XGBoost, Multi-Layer Perceptron, AdaBoost, Random Forest, Logistic Regression, Decision Tree, Bagging, LightGBM, Support Vector Machine, Extra Tree and Naive Bayes) are applied using Python language. Out of all algorithms applied, Support Vector Machine with PCA feature selection technique has given the highest accuracy value 99.66%, recall value 99.66%, precision value 99.67%, and f-score value 99.67%. To prove that results are statistically different, we have applied ANOVA one way test.
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