List of Articles feature selection

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  1 - Presenting A Hybrid Method of Deep Neural Networks to Prevent Intrusion in Computer Networks
  Mohsen Roknaldini Erfaneh Noroozi
  Introduction: Nowadays, computer networks have significant impacts on our daily lives, leading to cybersecurity becoming a crucial area of research. Cybersecurity techniques mainly encompass antivirus software, firewalls, and intrusion detection systems. Intrusion dete More

  Introduction: Nowadays, computer networks have significant impacts on our daily lives, leading to cybersecurity becoming a crucial area of research. Cybersecurity techniques mainly encompass antivirus software, firewalls, and intrusion detection systems. Intrusion detection system is one of the fundamental security tools in the field of computer networks and systems. The primary goal of an intrusion detection system is to identify and alert about any unauthorized activities, threats, or attacks on a system or network. By analyzing the flow of data and network/system events, the intrusion detection system attempts to identify patterns and indicators related to various attacks and intrusions. Intrusion detection systems can operate based on rules or learning. In the rule-based approach, algorithms and rules created by security experts and analysts are used to detect patterns and identify attacks. However, in the machine learning approach, machine learning algorithms and deep neural networks are employed to extract patterns and features related to attacks from real data. Method: This study focuses on the examination and presentation of a combined approach using deep neural networks to prevent intrusions in computer networks. The primary objective of this research is to enhance the efficiency of intrusion detection systems. To achieve this goal, a combined approach of deep learning and artificial neural networks is proposed. This approach utilizes deep neural networks to detect more complex features and improves the model's performance. Results: Simulation results demonstrate that deep neural network methods such as MLP, CNN, LSTM, and GRU yield favorable outcomes compared to other single-layer machine learning techniques. In this study, two combined methods, CNN-GRU and CNN-LSTM, were introduced and tested on the KDD CUP'99 dataset for comprehensive analysis and evaluation. Both combined approaches exhibit high accuracy and lower classification errors compared to other introduced methods. Therefore, it can be concluded that the CNN-LSTM combined approach performs well on the KDD CUP'99 dataset. Discussion: Based on the achieved results, the combined CNN-LSTM and CNN-GRU methods offer very good performance with accuracies of 99.95% and 99.92%, respectively, on the KDD CUP'99 dataset. Among these methods, minor differences in the performance of some parameters for classes may exist, yet both approaches remain acceptable. Hence, it can be concluded that the combined CNN-LSTM approach performs well on the KDD CUP'99 dataset. Manuscript profile
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  2 - An Online group feature selection algorithm using mutual information
  maryam rahmaninia sondos bahadori
  Introduction: In the area of big data, the dimension of data in many fields are increasing dramatically. To deal with the high dimensions of training data, online feature selection algorithms are considered as very important issue in data mining. Recently, online featur More

  Introduction: In the area of big data, the dimension of data in many fields are increasing dramatically. To deal with the high dimensions of training data, online feature selection algorithms are considered as very important issue in data mining. Recently, online feature selection methods have attracted a lot of attention from researchers. These algorithms deal with the process of selecting important and efficient features and removing redundant features without any pre-knowledge of the set of features. Despite all the progress in this field, there are still many challenges related to these algorithms. Among these challenges, we can mention scalability, minimum size of selected features, sufficient accuracy and execution time. On the other hand, in many real-world applications, features are entered into the dataset in groups and sequentially. Although many online feature selection algorithms have been presented so far, but none of them have been able to find trade of between these criteria. Method: In this paper, we propose a group online feature selection method with feature stream using two new measures of redundancy and relevancy using mutual information theory. Mutual information can compute linear and non-linear dependency between the variables. With the proposed method, we try to create a better tradeoff between all the challenges. Results: In order to show the effectiveness of the proposed online group feature selection method, a number of experiments have been conducted on six large multi-label training data sets named ALLAML, colon, SMK-CAN-187, credit-g, sonar and breast-cancer in different applications and 3 online group feature selection algorithms named FNE_OGSFS، Group-SAOLA and OGSFS which are presented recently. Also, 3 evaluation criteria including average accuracy using KNN (k - nearest neighborhood (, SVM (Support Vector Machine) and NB (Naïve Bayesian) classifiers, number of selected features and executing time were used as criteria for comparing the proposed method. According to the obtained results, the proposed algorithm has obtained better results in almost of cases compared to other algorithms which it shows the efficiency of the proposed method. Discussion: In this paper, we will show that proposed online group feature selection method will achieve better performance by considering label group dependency between the new arrival features. Manuscript profile
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  3 - A New Multi-Stage Feature Selection and Classification Approach: Bank Customer Credit Risk Scoring
  Farshid Abdi
  10.30495/jiei.2021.1919589.1079
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  4 - Smart car system: automobile driver's stress recognition with artificial neural networks
  Mahtab Vaezi Mehdi Nasri Farhad Azimifar
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