An Improved Feature Selection Algorithm Based on Ant Colony Optimization

Peng, Huijun; Ying, Chun; Tan, Shuhua; Hu, Bing; Sun, Zhixin

doi:10.1109/access.2018.2879583

Cited by 89 publications

(49 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The proposed technique produces 16 relevant features with a classification accuracy of 99.92%. While Peng et al [26] combine the Ant-Colony Optimization algorithm and feature selection, called FACO. The proposed work is able to produce features that improve the classification algorithm accuracy.…”

Section: Relevant Researchesmentioning

confidence: 99%

CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection

et al. 2020

View full text Add to dashboard Cite

Feature selection (FS) is one of the important tasks of data preprocessing in data analytics. The data with a large number of features will affect the computational complexity, increase a huge amount of resource usage and time consumption for data analytics. The objective of this study is to analyze relevant and significant features of huge network traffic to be used to improve the accuracy of traffic anomaly detection and to decrease its execution time. Information Gain is the most feature selection technique used in Intrusion Detection System (IDS) research. This study uses Information Gain, ranking and grouping the features according to the minimum weight values to select relevant and significant features, and then implements Random Forest (RF), Bayes Net (BN), Random Tree (RT), Naive Bayes (NB) and J48 classifier algorithms in experiments on CICIDS-2017 dataset. The experiment results show that the number of relevant and significant features yielded by Information Gain affects significantly the improvement of detection accuracy and execution time. Specifically, the Random Forest algorithm has the highest accuracy of 99.86% using the relevant selected features of 22, whereas the J48 classifier algorithm provides an accuracy of 99.87% using 52 relevant selected features with longer execution time.

show abstract

Section: Relevant Researchesmentioning

confidence: 99%

CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In traditional ant colony algorithm, the pheromone volatility factor is usually a fixed constant [34], [35]. In this paper, the dynamic pheromone volatilization factor (D-Rho) is used in the algorithm.…”

Section: E Design Of Volatilization Factor Of Dynamic Pheromonementioning

confidence: 99%

Smooth Path Planning of Ackerman Chassis Robot based on Improved ant Colony Algorithm

2020

International Journal of Circuits, Systems and Signal Processing

View full text Add to dashboard Cite

In the domain of robotics and autonomous driving, the automatic path planning of vehicle collision-free motion is an essential task on the navigation level. It is found that the traditional path planning algorithm and the ployline path cannot fully meet the driving requirements of Ackerman chassis robot. In order to solve the autonomous navigation problem of Ackerman chassis mobile robot in structured environment, this paper presents a new improved algorithm. The method of configuration space can introduce the robot's own structural size parameters into the algorithm. Through convex polygon detection method, the local U-shaped area in the map is transformed into a closed area. The essence of these two strategies is to preprocess the map. The initial pheromone distribution is no longer globally uniform, but is distributed according to the terrain. The volatilization factor of pheromone is changed from static constant to dynamic one, which is combined with Poisson distribution law. This strategy makes the improved pheromone distribution law not only avoid the randomness and blindness in the initial stage of the algorithm, but also ensure the ant colony's exploration behavior and guiding role in the middle stage of the algorithm. Path smoothing is also a challenging task. This algorithm optimizes the path step by step by improving the evaluation function, removing redundant nodes and 2-turning algorithm. Thus, a collision free smooth path suitable for Ackerman robot is obtained. This paper combines a variety of algorithm improvement strategies, not only improving the performance of ant colony algorithm path exploration, but also planning a smooth curve path suitable rather than polyline for Ackerman mobile robot tracking. The algorithm is coded and simulated by MATLAB, and the feasibility and effectiveness of the algorithm are verified. This will provide an important basis for the subsequent algorithm migration and lay the foundation for the path tracking control of the Ackermann chassis robot.

show abstract

“…A threshold correlation value of 0.20 is used to choose the relevant predictors in this paper. According to the correlation-based method, predictors 4,5,6,8,15,16,17,18,19, and 20 are selected to constitute the input variables for the PV power prediction.…”

Section: Selected Predictor Dataset Best Fitness (×10 -3 )mentioning

confidence: 99%

“…Various metaheuristic optimization techniques have been employed as search methods for IDI. For example, Particle Swarm Optimization (PSO) [4], Ant Colony Optimization (ACO) [5] and Genetic Algorithm (GA) [6]. GA has been widely used due to its higher suitability and effective searching capability.…”

Section: Introductionmentioning

confidence: 99%

Effective Input Dataset Identification Methodology for Accurate Prediction of Local PV Power Production

Eseye

Lehtonen

Tukia

et al. 2019

2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)

View full text Add to dashboard Cite

Local photovoltaic (PV) systems are playing a considerable role globally as a power resource and constituent element of the smart grid. Nevertheless, PVs may cause significant problems to the electric grid. This is due to the high variability of the PV power that is instigated by intermittent environmental conditions. Accurate prediction of PV power is very important to operate power grids containing high penetration of PVs. Most prior approaches have focused on forecasting the collective quantity of solar power generation at national or regional scale and disregarded the local PVs that are installed mainly for local electric supply. This paper devises an effective input dataset identification methodology (IDIM) to find the most significant and nonrepetitive input variables for accurate prediction of the power production of local PVs. In the devised methodology, the Binary Genetic Algorithm (BGA) is used for the input variable identification and Support Vector Regression (SVR) is employed for evaluating the fitness of the input datasets. The devised methodology is implemented and validated based on actual local PVs (building rooftop PVs) located in the Otaniemi area of Espoo, Finland. The results are compared with those obtained by conventional counterparts and manifest outperformed performances.

show abstract

An Improved Feature Selection Algorithm Based on Ant Colony Optimization

Cited by 89 publications

References 9 publications

CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection

CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection

Smooth Path Planning of Ackerman Chassis Robot based on Improved ant Colony Algorithm

Effective Input Dataset Identification Methodology for Accurate Prediction of Local PV Power Production

Contact Info

Product

Resources

About