A fast hybrid classification algorithm based on the minimum distance and the k-NN classifiers

2011 ◽  
Author(s):  
Stefanos Ougiaroglou ◽  
Georgios Evangelidis ◽  
Dimitris A. Dervos
Keyword(s):  
Minimum Distance ◽  
Classification Algorithm ◽  
Hybrid Classification

scholarly journals An Intrusion Detection Model based on Hybrid Classification algorithm

2018 ◽  
Vol 246 ◽  
pp. 03027
Author(s):  
Manfu Ma ◽  
Wei Deng ◽  
Hongtong Liu ◽  
Xinmiao Yun
Keyword(s):  
Intrusion Detection ◽  
Detection Rate ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithm ◽  
Data Set ◽  
Detection Model ◽  
Performance Requirements ◽  
Hybrid Classification ◽  
Bayes Algorithm

Due to using the single classification algorithm can not meet the performance requirements of intrusion detection, combined with the numerical value of KNN and the advantage of naive Bayes in the structure of data, an intrusion detection model KNN-NB based on KNN and Naive Bayes hybrid classification algorithm is proposed. The model first preprocesses the NSL-KDD intrusion detection data set. And then by exploiting the advantages of KNN algorithm in data values, the model calculates the distance between the samples according to the feature items and selects the K sample data with the smallest distance. Finally, by naive Bayes to get the final result. The experimental results on the NSL-KDD dataset show that the KNN-NB algorithm can meet the requirement of balanced performance than the traditional KNN and Naive Bayes algorithm in term of accuracy, sensitivity, false detection rate, specificity, and missed detection rate.

scholarly journals Prediction of Student Performance using Hybrid Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 6566-6570
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Student Performance ◽  
Research Work ◽  
Educational Data Mining ◽  
Classification Algorithm ◽  
Classification Algorithms ◽  
Data Types ◽  
Data Set ◽  
Hybrid Classification

Data mining technologies allow collection, storage and processing huge amounts of data and carrying a large variety of data types and samples. Predicting academic performance of student is the most successive research in this era. Previous research work researchers are used different classification algorithm to predict the student performance. There is lot of research work to be taken in the field of educational data mining and big data in education to increase the accuracy of the classification algorithm and predict the academic performance of student. In this research work we used hybrid classification algorithm for predicting the performance of students. Two Popular classification algorithms ID3 and J48 were applied on the data set. To make hybrid classification voting technique is applied using weka machine learning tool. In this work we tested how the hybrid algorithm accurately predicts the student data set. To check the predicted result classification accuracy was computed. This hybrid classification algorithm gives accuracy with 62.67%.

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