Dimensionality Reduction Using an Improved Whale Optimization Algorithm for Data Classification

Hegazy, Ah. E.; Makhlouf, Mohamed; El-Tawel, Gh. S.

doi:10.5815/ijmecs.2018.07.04

Cited by 16 publications

(5 citation statements)

References 29 publications

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“…Dimensionality reduction using improved whale optimization algorithm (IWOA) for data classification was proposed by Hegazy et al 7 This bio‐inspired optimization technique was introduced in 2016 which mainly imitates the hunting demeanor of humpback whales. Some modifications on the basic WOA structure were introduced to enhance the solution accuracy, reliability, and convergence speed.…”

Section: Related Workmentioning

confidence: 99%

Context adaptive ensemble classification mechanism with multi‐criteria decision making for network intrusion detection

Devprasad¹,

Ramanujam

Rajendran³

2022

Concurrency and Computation

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Nowadays, intrusion detection systems (IDSs) enabled with computational intelligence for electing the suitable classifiers to learn the patterns of various types of network attacks are under study. Selection mechanism of single or ensemble classifiers based on performance and ranking is to be enabled in the IDS framework to make accurate pattern predictions. A new anomaly based network intrusion detection system framework is proposed using context adaptive classification through the technique for order of preference by similarity to ideal solution (TOPSIS) ranking mechanism with hierarchy based chi-square and bat algorithms for feature selection.Parameters like accuracy, false positive rate (FPR), and classification model building time are explored for choosing the best and situation aware classifiers using TOPSIS.For experimentation, NSL-KDD and UNSW-NB15 datasets are used. The proposed system opts decision tree (DT) algorithm for NSL-KDD and produces 98.77% accuracy, 0.03% FPR with 8 features. Ensemble learner is selected for the UNSW-NB15 dataset using DT and support vector machine classifiers with 9 features. Combined classifier predicts 89.43% accuracy and 3.215% FPR. Experimentation on the proposed methodology with the state of the art approaches produces promising results.

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Section: Related Workmentioning

confidence: 99%

Context adaptive ensemble classification mechanism with multi‐criteria decision making for network intrusion detection

Devprasad¹,

Ramanujam

Rajendran³

2022

Concurrency and Computation

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“…This approach yielded high accuracy. Some more research work on the neural network and knowledge reductions are mentioned in ref [29][30][31][32][33][34][35][36][37].…”

Section: Related Workmentioning

confidence: 99%

SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection

et al. 2020

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The enormous growth in internet usage has led to the development of different malicious software posing serious threats to computer security. The various computational activities carried out over the network have huge chances to be tampered and manipulated and this necessitates the emergence of efficient intrusion detection systems. The network attacks are also dynamic in nature, something which increases the importance of developing appropriate models for classification and predictions. Machine learning (ML) and deep learning algorithms have been prevalent choices in the analysis of intrusion detection systems (IDS) datasets. The issues pertaining to quality and quality of data and the handling of high dimensional data is managed by the use of nature inspired algorithms. The present study uses a NSL-KDD and KDD Cup 99 dataset collected from the Kaggle repository. The dataset was cleansed using the min-max normalization technique and passed through the 1-N encoding method for achieving homogeneity. A spider monkey optimization (SMO) algorithm was used for dimensionality reduction and the reduced dataset was fed into a deep neural network (DNN). The SMO based DNN model generated classification results with 99.4% and 92% accuracy, 99.5%and 92.7% of precision, 99.5% and 92.8% of recall and 99.6%and 92.7% of F1-score, utilizing minimal training time. The model was further compared with principal component analysis (PCA)-based DNN and the classical DNN models, wherein the results justified the advantage of implementing the proposed model over other approaches.

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“…Owing to its impressive advantages such as easy implementation, lesser adjustable parameters and quick convergence, the WOA has been successfully applied to diverse problems. For example, 0-1 knapsack problem [16], the permutation flow shop scheduling problem [17], clustering and classification [18], [19], optimal control problems [20], [21], Multi-objective optimization [22], [23], routing optimization [24], support vector machines and neural networks [25]- [27], Feature selection [28], economic load dispatch problems [29], image segmentation [30], fuzzy controller [31], and parameter estimation [32].…”

Section: Metaheuristic Is Formally Defined As An Iterative Generationmentioning

confidence: 99%

Whale Optimization Algorithm Based on Lamarckian Learning for Global Optimization Problems

Zhang

Liu

2019

IEEE Access

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Whale optimization algorithm (WOA) is a population-based meta-heuristic imitating the hunting behavior of humpback whales, which has been successfully applied to solve many real-world problems. Although WOA has a good convergence rate, it cannot achieve good results in finding the global optimal solution of high-dimensional complex optimization problems. The learning mechanism of Lamarckian evolutionism has the advantages of speeding up and strengthening local search. Through this learning mechanism, solutions with certain conditions can acquire higher adaptability with a higher probability by active learning. To enhance the global convergence speed and get better performance, this paper presents a WOA based on Lamarckian learning (WOALam) for solving high-dimensional function optimization problems. First, the population is initialized by good point set theory so that individuals can be evenly distributed in the solution space. Second, the upper confidence bound algorithm is used to calculate the development potential of the individual. Finally, based on the evolutionary theory of Lamarck, individuals with more development potentials are selected to perform the local enhanced search to improve the performance of the algorithm. The WOALam was compared with six variants of WOA on 44 benchmark functions. The experiments proved that the proposed algorithm can balance the global exploring ability and the exploiting ability well. It could obtain better results with fewer iterations and had good convergence speed and accuracy.INDEX TERMS Whale optimization algorithm, Lamarckian learning, good point set, upper confidence bound, optimization.

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Dimensionality Reduction Using an Improved Whale Optimization Algorithm for Data Classification

Cited by 16 publications

References 29 publications

Context adaptive ensemble classification mechanism with multi‐criteria decision making for network intrusion detection

Context adaptive ensemble classification mechanism with multi‐criteria decision making for network intrusion detection

SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection

Whale Optimization Algorithm Based on Lamarckian Learning for Global Optimization Problems

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