A Granular Computing Based Decision Tree Algorithm and its Application in Intrusion Detection

2012 ◽  
Vol 268-270 ◽  
pp. 1730-1734
Author(s):  
Cheng Hua Wang ◽  
Lin Zhou ◽  
Feng Jiang ◽  
Hong Bo Zhao
Keyword(s):  
Intrusion Detection ◽  
Decision Tree ◽  
Granular Computing ◽  
Conditional Entropy ◽  
Decision Tree Algorithm ◽  
Entropy Model ◽  
Approximation Accuracy ◽  
Data Set ◽  
Tree Algorithm ◽  
Tree Algorithms

Decision tree algorithms have been widely used in intrusion detection. In this paper, within the framework of granular computing (GrC), we propose a new decision tree algorithm called DTGAE and apply it to intrusion detection. First, by virtue of the GrC model using information tables, we propose a new information entropy model, which contains two basic notions: approximation entropy of granule (AEG) and GrC-based approximation entropy (GAE), where the latter is defined based on the former. In algorithm DTGAE, GAE is adopted as the heuristic information for the selection of splitting attributes. When calculating AEG and GAE, we not only utilize the concept of conditional entropy in Shannon's information theory, but also use the concept of approximation accuracy in rough sets. Second, we present a method of decision tree pre-pruning based on Düntsch's knowledge entropy. Finally, the KDDCUP99 data set is used to verify the effectiveness of our algorithm in intrusion detection.

scholarly journals COMPARATIVE STUDY OF MACHINE LEARNING KNN, SVM, AND DECISION TREE ALGORITHM TO PREDICT STUDENT’S PERFORMANCE

2019 ◽  
Vol 7 (1) ◽  
pp. 190-196
Author(s):  
Slamet Wiyono ◽  
Taufiq Abidin
Keyword(s):  
Decision Tree ◽  
Student Performance ◽  
Prediction Accuracy ◽  
Model Building ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Svm Algorithm ◽  
Tree Algorithms ◽  
Student’S Performance ◽  
Predicting Student Performance

Students who are not-active will affect the number of students who graduate on time. Prevention of not-active students can be done by predicting student performance. The study was conducted by comparing the KNN, SVM, and Decision Tree algorithms to get the best predictive model. The model making process was carried out by steps; data collecting, pre-processing, model building, comparison of models, and evaluation. The results show that the SVM algorithm has the best accuracy in predicting with a precision value of 95%. The Decision Tree algorithm has a prediction accuracy of 93% and the KNN algorithm has a prediction accuracy value of 92%.

scholarly journals CUDT: A CUDA Based Decision Tree Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Win-Tsung Lo ◽  
Yue-Shan Chang ◽  
Ruey-Kai Sheu ◽  
Chun-Chieh Chiu ◽  
Shyan-Ming Yuan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
New Technology ◽  
Large Data ◽  
Decision Tree Algorithm ◽  
Data Set ◽  
Tree Algorithm ◽  
Ubiquitous Sensing ◽  
Device Architecture ◽  
Huge Data

Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree. However, those algorithms are developed and run on traditional distributed systems. Obviously the latency could not be improved while processing huge data generated by ubiquitous sensing node in the era without new technology help. In order to improve data processing latency in huge data mining, in this paper, we design and implement a new parallelized decision tree algorithm on a CUDA (compute unified device architecture), which is a GPGPU solution provided by NVIDIA. In the proposed system, CPU is responsible for flow control while the GPU is responsible for computation. We have conducted many experiments to evaluate system performance of CUDT and made a comparison with traditional CPU version. The results show that CUDT is 5∼55 times faster than Weka-j48 and is 18 times speedup than SPRINT for large data set.

scholarly journals Identifying the Risky SNP of Osteoporosis with ID3-PEP Decision Tree Algorithm

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Jincai Yang ◽  
Huichao Gu ◽  
Xingpeng Jiang ◽  
Qingyang Huang ◽  
Xiaohua Hu ◽  
...  
Keyword(s):  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Data Set ◽  
Tree Algorithm ◽  
The Past ◽  
Random Walk Algorithm ◽  
Number Of Genes ◽  
And Function ◽  
Positive Results ◽  
Walk Algorithm

In the past 20 years, much progress has been made on the genetic analysis of osteoporosis. A number of genes and SNPs associated with osteoporosis have been found through GWAS method. In this paper, we intend to identify the suspected risky SNPs of osteoporosis with computational methods based on the known osteoporosis GWAS-associated SNPs. The process includes two steps. Firstly, we decided whether the genes associated with the suspected risky SNPs are associated with osteoporosis by using random walk algorithm on the PPI network of osteoporosis GWAS-associated genes and the genes associated with the suspected risky SNPs. In order to solve the overfitting problem in ID3 decision tree algorithm, we then classified the SNPs with positive results based on their features of position and function through a simplified classification decision tree which was constructed by ID3 decision tree algorithm with PEP (Pessimistic-Error Pruning). We verified the accuracy of the identification framework with the data set of GWAS-associated SNPs, and the result shows that this method is feasible. It provides a more convenient way to identify the suspected risky SNPs associated with osteoporosis.

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