• Title/Summary/Keyword: Decision tree method

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Hybridized Decision Tree methods for Detecting Generic Attack on Ciphertext

  • Alsariera, Yazan Ahmad
    • International Journal of Computer Science & Network Security
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    • v.21 no.7
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    • pp.56-62
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    • 2021
  • The surge in generic attacks execution against cipher text on the computer network has led to the continuous advancement of the mechanisms to protect information integrity and confidentiality. The implementation of explicit decision tree machine learning algorithm is reported to accurately classifier generic attacks better than some multi-classification algorithms as the multi-classification method suffers from detection oversight. However, there is a need to improve the accuracy and reduce the false alarm rate. Therefore, this study aims to improve generic attack classification by implementing two hybridized decision tree algorithms namely Naïve Bayes Decision tree (NBTree) and Logistic Model tree (LMT). The proposed hybridized methods were developed using the 10-fold cross-validation technique to avoid overfitting. The generic attack detector produced a 99.8% accuracy, an FPR score of 0.002 and an MCC score of 0.995. The performances of the proposed methods were better than the existing decision tree method. Similarly, the proposed method outperformed multi-classification methods for detecting generic attacks. Hence, it is recommended to implement hybridized decision tree method for detecting generic attacks on a computer network.

An Application of Decision Tree Method for Fault Diagnosis of Induction Motors

  • Tran, Van Tung;Yang, Bo-Suk;Oh, Myung-Suck
    • Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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    • 2006.11a
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    • pp.54-59
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    • 2006
  • Decision tree is one of the most effective and widely used methods for building classification model. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining have considered the decision tree method as an effective solution to their field problems. In this paper, an application of decision tree method to classify the faults of induction motors is proposed. The original data from experiment is dealt with feature calculation to get the useful information as attributes. These data are then assigned the classes which are based on our experience before becoming data inputs for decision tree. The total 9 classes are defined. An implementation of decision tree written in Matlab is used for these data.

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Typical Classification of Rural Area Considering Settlement Environment by Decision Tree Method (정주여건을 고려한 의사결정나무기법 활용 농촌지역 유형화)

  • Bae, Seung-Jong;Kim, Dae-Sik;Eun, Sang-Kyu
    • Journal of The Korean Society of Agricultural Engineers
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    • v.58 no.6
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    • pp.79-92
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    • 2016
  • The objective of this study is to classify the types of rural areas (138 $si{\cdot}gun$) considering settlement environment by Decision Tree Method (CHAID). The CHAID method was used for decision tree algorithm and the seven dependant variables and 5 explanatory variables were selected, respectively. By decision tree method, rural areas were finally classified into six groups through three separate processes. City area, lower area in aging rate and higher area in farmland area ratio was analyzed to be relatively rich rather than other area in the case of settlement environment index. In the future, this study will be able to utilize as a reference to the planning of rural development projects.

Classification Accuracy Improvement for Decision Tree (의사결정트리의 분류 정확도 향상)

  • Rezene, Mehari Marta;Park, Sanghyun
    • Proceedings of the Korea Information Processing Society Conference
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    • 2017.04a
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    • pp.787-790
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    • 2017
  • Data quality is the main issue in the classification problems; generally, the presence of noisy instances in the training dataset will not lead to robust classification performance. Such instances may cause the generated decision tree to suffer from over-fitting and its accuracy may decrease. Decision trees are useful, efficient, and commonly used for solving various real world classification problems in data mining. In this paper, we introduce a preprocessing technique to improve the classification accuracy rates of the C4.5 decision tree algorithm. In the proposed preprocessing method, we applied the naive Bayes classifier to remove the noisy instances from the training dataset. We applied our proposed method to a real e-commerce sales dataset to test the performance of the proposed algorithm against the existing C4.5 decision tree classifier. As the experimental results, the proposed method improved the classification accuracy by 8.5% and 14.32% using training dataset and 10-fold crossvalidation, respectively.

A study of constitution diagnosis using decision tree method (의사결정나무법을 이용한 체질진단에 관한 연구)

  • Lee, Yong-Seop;Park, Seong-Sik;Park, Eun-Kyung
    • Journal of Sasang Constitutional Medicine
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    • v.13 no.2
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    • pp.144-155
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    • 2001
  • By the increasing concern about Sasang Constitution Medicine, its practical use is considered very important in disease prevention and medical treatment. However, the method of constitution classification is depending on the doctor's clinical trials because of the lack of the objective test criteria. This study is trying to improve the objectiveness of diagnosis using a new statistical method, decision tree. Decision tree method-a classification technique in the statistical analysis- was used to analyze the result of QSCCII instead of using discriminant analysis. As a result, 16 among 121 QSCCII questions was selected as important questions and 21 terminal nodes was built to classify the constitution. Using only 16 questions shown in the result of decision tree, we can diagnose and interpret the constitution easily and effectively.

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Decision Tree with Optimal Feature Selection for Bearing Fault Detection

  • Nguyen, Ngoc-Tu;Lee, Hong-Hee
    • Journal of Power Electronics
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    • v.8 no.1
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    • pp.101-107
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    • 2008
  • In this paper, the features extracted from vibration time signals are used to detect the bearing fault condition. The decision tree is applied to diagnose the bearing status, which has the benefits of being an expert system that is based on knowledge history and is simple to understand. This paper also suggests a genetic algorithm (GA) as a method to reduce the number of features. In order to show the potentials of this method in both aspects of accuracy and simplicity, the reduced-feature decision tree is compared with the non reduced-feature decision tree and the PCA-based decision tree.

Evaluation Method of College English Education Effect Based on Improved Decision Tree Algorithm

  • Dou, Fang
    • Journal of Information Processing Systems
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    • v.18 no.4
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    • pp.500-509
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    • 2022
  • With the rapid development of educational informatization, teaching methods become diversified characteristics, but a large number of information data restrict the evaluation on teaching subject and object in terms of the effect of English education. Therefore, this study adopts the concept of incremental learning and eigenvalue interval algorithm to improve the weighted decision tree, and builds an English education effect evaluation model based on association rules. According to the results, the average accuracy of information classification of the improved decision tree algorithm is 96.18%, the classification error rate can be as low as 0.02%, and the anti-fitting performance is good. The classification error rate between the improved decision tree algorithm and the original decision tree does not exceed 1%. The proposed educational evaluation method can effectively provide early warning of academic situation analysis, and improve the teachers' professional skills in an accelerated manner and perfect the education system.

Fault Diagnosis of Induction Motors using Decision Trees (결정목을 이용한 유도전동기 결함진단)

  • Tran Van Tung;Yang Bo-Suk;Oh Myung-Suck
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2006.11a
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    • pp.407-410
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    • 2006
  • Decision tree is one of the most effective and widely used methods for building classification model. Researchers from various disciplines such as statistics, machine teaming, pattern recognition, and data mining have considered the decision tree method as an effective solution to their field problems. In this paper, an application of decision tree method to classify the faults of induction motors is proposed. The original data from experiment is dealt with feature calculation to get the useful information as attributes. These data are then assigned the classes which are based on our experience before becoming data inputs for decision tree. The total 9 classes are defined. An implementation of decision tree written in Matlab is used for four data sets with good performance results

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Adopting and Implementation of Decision Tree Classification Method for Image Interpolation (이미지 보간을 위한 의사결정나무 분류 기법의 적용 및 구현)

  • Kim, Donghyung
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.16 no.1
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    • pp.55-65
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    • 2020
  • With the development of display hardware, image interpolation techniques have been used in various fields such as image zooming and medical imaging. Traditional image interpolation methods, such as bi-linear interpolation, bi-cubic interpolation and edge direction-based interpolation, perform interpolation in the spatial domain. Recently, interpolation techniques in the discrete cosine transform or wavelet domain are also proposed. Using these various existing interpolation methods and machine learning, we propose decision tree classification-based image interpolation methods. In other words, this paper is about the method of adaptively applying various existing interpolation methods, not the interpolation method itself. To obtain the decision model, we used Weka's J48 library with the C4.5 decision tree algorithm. The proposed method first constructs attribute set and select classes that means interpolation methods for classification model. And after training, interpolation is performed using different interpolation methods according to attributes characteristics. Simulation results show that the proposed method yields reasonable performance.

Selection of the Optimal Decision Tree Model Using Grid Search Method : Focusing on the Analysis of the Factors Affecting Job Satisfaction of Workplace Reserve Force Commanders (격자탐색법을 이용한 의사결정나무 분석 최적 모형 선택 : 직장예비군 지휘관의 직장만족도에 대한 영향 요인 분석을 중심으로)

  • Jeong, Chulwoo;Jeong, Won Young;Shin, David
    • Journal of the Korean Operations Research and Management Science Society
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    • v.40 no.2
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    • pp.19-29
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    • 2015
  • The purpose of this study is to suggest the grid search method for selecting an optimal decision tree model. It chooses optimal values for the maximum depth of tree and the minimum number of observations that must exist in a node in order for a split to be attempted. Therefore, the grid search method guarantees building a decision tree model that shows more precise and stable classifying performance. Through empirical analysis using data of job satisfaction of workplace reserve force commanders, we show that the grid search method helps us generate an optimal decision tree model that gives us hints for the improvement direction of labor conditions of Korean workplace reserve force commanders.