Data Mining - Concepts and Techniques.

The task of developing Intrusion Detection System (IDS) crucially depends on the preprocessing along with selecting important data features of it. Another crucial factor is design of efficient learning algorithm that classify normal and anomalous patterns. The objective of this research work is to propose a new and better version of the Naive Bayes classifiers that improves the accuracy of intrusion detection in IDS. The proposed classifier is also supposed to take less time as compared with the existing classifiers. To gain better accuracy and fast processing of network traffic, this study applied three standard methods of feature selection. This study tested the performance of the new proposed classifier algorithm with existing classifiers, namely Naive bayes, J48 and REPTree thereby measuring different performance parameters using 10-fold cross validation. This study evaluates the performance of the new proposed classifier algorithm by using NSL-KDD data set. Empirical results of our study show that the proposed updated version of the Naive Bayes classifiers gives better results in terms of intrusion detection and false alarm rate.

/pdf/network-intrusion-detection-with-feature-selection-1xj3axo37t.pdf

Network Intrusion Detection with Feature Selection Techniques using Machine-Learning Algorithms

In this paper, a differential-evolution-based approach implementing a global search strategy to find a near-optimal axis-parallel decision tree is introduced. In this paper, the internal nodes of a decision tree are encoded in a real-valued chromosome, and a population of them evolves using the training accuracy of each one as its fitness value. The height of a complete binary decision tree whose number of internal nodes is not less than the number of attributes in the training set is used to compute the chromosome size, and a procedure to map a feasible axis-parallel decision tree from one chromosome is applied, which uses both the smallest-position-value rule and the training instances. The best decision tree in the final population is refined replacing some leaf nodes with sub-trees to improve its accuracy. The differential evolution algorithm has been successfully applied in conjunction with several supervised learning methods to solve numerous classification problems, due to it exhibiting a good tradeoff between its exploitation and exploration skills, and to the best of our knowledge, it has not been utilized to build axis-parallel decision trees. To obtain reliable estimates of the predictive performance of this approach and to compare its results with those achieved by other methods, a repeated stratified ten-fold cross-validation procedure is applied in the experimental study. A statistical analysis of these results suggests that our approach is better as a decision tree induction method as compared with other supervised learning methods. Also our results are comparable to those obtained with random forest and one multilayer-perceptron-based classifier.

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Construction of Near-Optimal Axis-Parallel Decision Trees Using a Differential-Evolution-Based Approach

Received: 23 October 2019 Accepted: 5 February 2020 Due to the increasing usage of multimedia and storage devices accessible, searching for large image databases has become imperative. Furthermore, the handiness of high-speed internet has escalated the exchange of images by users enormously. Content-Based Image Retrieval is proposed in this work, taking features based on Exact Legendre Moments, HVS color quantization with dc coefficient and statistical properties such as variance, mean, and skew of Conjugate Symmetric Sequency Complex Hadamard Transform (CS-SCHT). In most of the machine learning tasks, the quality of the learning process depends on dimensionality. High dimensional datasets can influence the classification outcome and training time. To overcome this problem, we use DE (Differential Evolution) to generate the optimal feature subsets. The features scaled by weights derived from the firefly algorithm, which fed to Multi-Class SVM. The fitness function taken for the firefly algorithm is the classification error of SVM. By minimizing fitness function, optimum weights are obtained. When these optimal weights are applied to SVM, the proposed algorithm exhibits better precision, recall, and accuracy when compared to some of the existing algorithms in the literature.

/pdf/a-multi-class-svm-based-content-based-image-retrieval-system-4cbpck2p4e.pdf

A Multi-class SVM Based Content Based Image Retrieval System Using Hybrid Optimization Techniques

This paper describes the application of a Differential Evolution based approach for inducing oblique decision trees in a recursive partitioning strategy Considering that: (1) the task of finding an optimal hyperplane with real-valued coefficients is a complex optimization problem in a continuous space, and (2) metaheuristics have been successfully applied for solving this type of problems, in this work a differential evolution algorithm is applied with the aim of finding near-optimal hyperplanes that will be used as test conditions of an oblique decision tree The experimental results show that this approach induces more accurate classifiers than those produced by other proposed induction methods

OC1-DE: A Differential Evolution Based Approach for Inducing Oblique Decision Trees

This paper addresses the problem of SVM classification optimisation. For this purpose, the authors propose an SVM classification system based on differential evolution DE to improve the generalisation performance of the SVM classifier. In the classification system, a method of simultaneous parameters optimisation and feature selection for support vector machine is put forward. The experiments are conducted on the basis of benchmark dataset. The obtained results clearly confirm the superiority of the DE-SVM-FS approach compared to default SVM classifier and DE-SVM algorithm; this suggests that further substantial improvements in terms of classification accuracy can be achieved by the proposed DE-SVM-FS classification system.

Differential evolution-based parameters optimisation and feature selection for support vector machine

Naive Bayes (NB) classifier is a simple and efficient classifier, but the independent assumption of its attribute limits the application of the actual data. This paper presents an approach called particle swarm optimization-naive Bayes (PSO-NB) which takes advantage of combination particle swarm optimization with naive Bayes for attribute selection to improve naive Bayes classifier. This method applies PSO firstly to search out an optimal subset of attributes reduction in the original attribute space, and then constructs a naive Bayes classifier on the gotten subset of the attributes reduction. Nineteen experimental results on UCI datasets distinctly show that compared with Cfs-BestFirst algorithm, NB algorithm, Decision Tree(C4.5) algorithm, K-neighbor(KNN) algorithm, the proposed algorithm has higher classification accuracy.

/pdf/a-novel-naive-bayes-classification-algorithm-based-on-1ckwvd7rox.pdf

A Novel Naive Bayes Classification Algorithm Based on Particle Swarm Optimization

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Differential evolution-based parameters optimisation and feature selection for support vector machine

A Novel Naive Bayes Classification Algorithm Based on Particle Swarm Optimization