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[PDF] Top 20 An Efficient Decision Tree Model for Classification of Attacks with Feature Selection

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An Efficient Decision Tree Model for Classification of Attacks with Feature Selection

An Efficient Decision Tree Model for Classification of Attacks with Feature Selection

... on decision tree techniques with special reference to feature ...based decision tree techniques are initially applied on NSL-KDD data set using k-fold validation and 5 different ... See full document

7

An Ensemble Model for Classification of Attacks with Feature Selection based on KDD99 and NSL KDD Data Set

An Ensemble Model for Classification of Attacks with Feature Selection based on KDD99 and NSL KDD Data Set

... (GR) feature selection ...individual classification techniques and its ensemble model on KDD99 and NSL-KDD data set to check the robustness of ...Ratio feature selection ... See full document

6

Intrusion Detection System using Recurrent Neural Network with Deep Learning

Intrusion Detection System using Recurrent Neural Network with Deep Learning

... different attacks in the ...IDS, Feature Selection method has been applied to identify a suitable subset of ...IDS model is trained using NSL-KDD Dataset for both binary and multiclass ...The ... See full document

9

Anomaly Detection in Computer Networks By using Machine Learning Algorithms

Anomaly Detection in Computer Networks By using Machine Learning Algorithms

... [1]. Efficient intrusion detection is needed as a defense of the network system to detect the attacks over the ...A feature selection and classification based Intrusion Detection ... See full document

5

Improved Decision Tree Induction Algorithm with Feature Selection, Cross Validation, Model Complexity and Reduced Error Pruning

Improved Decision Tree Induction Algorithm with Feature Selection, Cross Validation, Model Complexity and Reduced Error Pruning

... like Decision Tree with Naive Bayes, KNN, SVM, Neural Networks and Bayesian ...which classification learning algorithm is superior to others, but under which conditions a particular method can ... See full document

5

SOFTWARE CONFIGURATION MANAGEMENT PRACTICE IN MALAYSIA

SOFTWARE CONFIGURATION MANAGEMENT PRACTICE IN MALAYSIA

... a model which has an ability to classify data dynamically according to the security form low till high ...and classification method. The purpose of classification is to apply a complex security ... See full document

9

A Composite Hybrid Feature Selection Learning- Based Optimization of Genetic Algorithm For Breast Cancer Detection

A Composite Hybrid Feature Selection Learning- Based Optimization of Genetic Algorithm For Breast Cancer Detection

... synthetic model set of features focused on the optimization of the genetic algorithm (CHFS-BOGA) to forecast breast ...hybrid feature selection approach combines the advantages of three filter ... See full document

21

Feature Selection with Data Re Construction of Standardized Search with Decision Tree

Feature Selection with Data Re Construction of Standardized Search with Decision Tree

... statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, ... See full document

5

Building an Effective Intrusion Detection System using combined Signature and Anomaly Detection Techniques

Building an Effective Intrusion Detection System using combined Signature and Anomaly Detection Techniques

... detection model and an anomaly based detection ...detection model known normal traffic information is ...based feature selection using genetic algorithm and J48 (C4.5) decision ... See full document

7

Cramers V Test Discretization Based Spatial Decision Tree Learning for Land Cover Classification

Cramers V Test Discretization Based Spatial Decision Tree Learning for Land Cover Classification

... spatial decision tree learning models is used to estimate the decision tree classifier that minimizes classification errors as well as salt-and-pepper ...cover classification for ... See full document

8

Computational Framework for Heart Disease Prediction using Deep Belief Neural Network with Fuzzy Logic

Computational Framework for Heart Disease Prediction using Deep Belief Neural Network with Fuzzy Logic

... prediction model for Heart Disease through the hybridization of genetic algorithm with Decision Tree for feature subset selection, Genetic with Naive Bayes and Genetic with ... See full document

9

A Hybrid Intrusion Detection System Based on C5.0 Decision Tree Algorithm and One-Class SVM with CFA

A Hybrid Intrusion Detection System Based on C5.0 Decision Tree Algorithm and One-Class SVM with CFA

... or attacks to protect internal ...proposed feature selection algorithm based on CFA is used to find the best optimal subset selection, ...C5.0 decision tree classifier is used to ... See full document

12

An Opinion Mining By Manhattan Clustering Using Decision Tree Feature Selection

An Opinion Mining By Manhattan Clustering Using Decision Tree Feature Selection

... Feature selection is needed for successful data mining applications, as they lower data dimensionality removing irrelevant ...a feature selection for Opinion mining using decision ... See full document

7

Decision tree SVM model with Fisher feature selection for speech emotion recognition

Decision tree SVM model with Fisher feature selection for speech emotion recognition

... In decision tree GMM method, GMM, like SVM, is used to classify emotions at decision tree nodes, and the features after Fisher selection are used to train decision tree ... See full document

14

Feature Selection Using Decision Tree Induction in Class level Metrics Dataset for Software Defect Predictions

Feature Selection Using Decision Tree Induction in Class level Metrics Dataset for Software Defect Predictions

... for feature selection is proposed based on Decision Tree ...on decision tree classifiers used in the classification ...relevant feature set or new feature ... See full document

6

A Hybrid Classification Model For Diabetes Dataset Using Decision Tree

A Hybrid Classification Model For Diabetes Dataset Using Decision Tree

... The tree partition or clustering module is used to eliminate the redundant ...spanning tree is constructed the features are divided into clusters by using graph-theoretic clustering ...called tree ... See full document

7

A survey on feature selection to perform classification using Meta Heuristic algorithms in Data Mining Domain

A survey on feature selection to perform classification using Meta Heuristic algorithms in Data Mining Domain

... different Classification Techniques in data mining. Decision trees and Bayesian Network (BN) generally have different operational profiles, when one is very accurate the other is not and vice ...contrary, ... See full document

12

II. DECISION TREE CLASSIFICATION

II. DECISION TREE CLASSIFICATION

... small-size tree which is constructed in a short time, using a small amount of ...the classification goal and some problems which must be ...to decision tree construction in section ... See full document

5

Scene Classification Using Efficient Low-level Feature Selection 1

Scene Classification Using Efficient Low-level Feature Selection 1

... Abstract—With the development of digital cameras, the digital photographs were flooded in our life. How to classify images efficiently in huge image database becomes an important research topic. In recent years, the ... See full document

5

A Survey on Detection of Breast Cancer from Mammogram

A Survey on Detection of Breast Cancer from Mammogram

... The author W.Y. Cheng et. al presented a paper based on finding a robust predictive model to identify the breast cancer disease. For the identification purpose used the ample training dataset which is the samples ... See full document

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