Three-way selection random forest algorithm based on decision boundary entropy

Zhang, Chunying; Ren, Jing; Liu, Fengchun; Li, Xiaoqi; Liu, Shouyue

doi:10.1007/s10489-021-03033-7

Cited by 8 publications

(5 citation statements)

References 40 publications

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“…There is uncertainty in the data information, and information entropy and approximate classification quality are measures of uncertain information. As a result, the attribute importance defined by combining information entropy, attribute dependence, and approximate classification quality is used to measure the attribute [13]. We improved the three-way attribute random selection algorithm in [13] to obtain a new reduction method, thus achieving the purpose of dimension reduction.…”

Section: Attribute Reductionmentioning

confidence: 99%

“…Calculate g(c) When determining the number of attributes, if the number of attributes is too much, the effect of dimension reduction cannot be achieved; if the number of attributes is too small, the model accuracy may be affected. In reference [13], the authors take √ p attributes to train a subtree and obtain a random forest. If it is obviously not appropriate to continue to select √ p attributes, here, half of the original attributes are selected as the number of attributes after attribute selection.…”

Section: Three-way Attribute Reductionmentioning

confidence: 99%

“…The attributes in the boundary domain also contributed to the decimation result and it is obviously not appropriate to delete them directly, so the attributes in the boundary domain need to be taken into account. The decision boundary entropy proposed by Zhang [13] can not only consider the information in the boundary domain but also consider the information entropy, which can measure the uncertain information. Then, they defined a new measure of attribute importance, finally proposed a three-way attribute random selection algorithm to handle the dataset in random forest, and achieved good results in the intrusion detection datasets [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SOINN Intrusion Detection Model Based on Three-Way Attribute Reduction

Ren,

Liu,

Huang

et al. 2023

Electronics

Self Cite

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With a large number of intrusion detection datasets and high feature dimensionality, the emergent nature of new attack types makes it impossible to collect network traffic data all at once. The modified three-way attribute reduction method is combined with a Self-Organizing Incremental learning Neural Network (SOINN) algorithm to propose a self-organizing incremental neural network intrusion detection model based on three-way attribute reduction. Attribute importance is used to perform attribute reduction, and the data after attribute reduction are fed into a self-organized incremental learning neural network algorithm, which generalizes the topology of the original data through self-organized competitive learning. When the streaming data are transferred into the model, the inter-class insertion or node fusion operation is performed by comparing the inter-node distance and similarity threshold to achieve incremental learning of the model streaming data. The inter-node distance value is introduced into the weight update formulation to replace the traditional learning rate and to optimize the topological structure adjustment operation. The experimental results show that T-SOINN achieves high precision and recall when processing intrusion detection data.

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Section: Attribute Reductionmentioning

confidence: 99%

Section: Three-way Attribute Reductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SOINN Intrusion Detection Model Based on Three-Way Attribute Reduction

Ren,

Liu,

Huang

et al. 2023

Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Zhang et al proved the most direct simplified version of the original RF, that is, when selecting split features and split values, it randomly selects one of all features as split feature, and at the same time, it randomly selects one value from the selected feature value as split value [16]. Bao ey connected RF to the representation learning process of the deep neural network and turned the split function into a random decision function, so that it could update the parameters of the whole network through backpropagation [18]. He et al also proposed a depth structure based entirely on RF [19].…”

Section: Research Status Of Rf Algorithmmentioning

confidence: 99%

Research on Development Strategy of Ethnic Sports Tourism Resources Based on Stochastic Forest Algorithm

Nie

2022

Mobile Information Systems

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The interactive development mode of combining sports industry with tourism industry has attracted more and more people’s attention and gradually been accepted by people. The organic combination of the two can effectively promote the healthy growth of regional economy. The development and operation mode of minority STRs (sports tourism resources) has become an important topic in the research of sports tourism development. It is not only important for the successful integration of minority traditional sports into tourism industry but also of great significance for promoting the development of sports tourism industry. In this paper, the personalized sports tourism service recommendation system based on multi-objective optimization is studied, and a tourism service combination method based on multi-objective optimization is proposed. This method is based on multi-objective optimization, and the historical data of tourists and their current preferences are considered, respectively. The converted data are used to train the RF (random forest) model offline, and online recommendation only needs to be scored and predicted according to the rules of the RF model. The results show that the online recommendation time of the proposed algorithm is basically below 100 s, which is much lower than that of other recommendation algorithms. The experimental results show that setting the weights of user evaluation information and related tourism information can further improve the matching degree between recommendation results and users’ needs.

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“…However, the inherent randomness in the attribute selection process during the construction of a random forest can result in suboptimal decision tree performance. To address this issue, document [11] proposes a novel approach based on decision boundary entropy, which selects three branches for each attribute during decision tree construction. This method improves the accuracy of individual decision trees and subsequently, the overall classification performance of the forest.…”

Section: Introductionmentioning

confidence: 99%

Three-Way Selection Random Forest Optimization Model for Anomaly Traffic Detection

Zhang

Yang

et al. 2023

Electronics

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With the application and development of Internet technology, network traffic is growing rapidly, and the situation of network security is becoming more and more serious. As an important way to protect network security, abnormal traffic detection has been paid more and more attention. In this paper, the uncertainty of the samples in the abnormal traffic detection dataset is studied. Combining the three-way decision idea with the random forest algorithm, a three-way selection random forest optimization model for abnormal traffic detection is proposed. Firstly, the three-way decision idea is integrated into the random selection process of feature attributes, and the attribute importance based on decision boundary entropy is calculated. The feature attributes are divided into the normal domain, abnormal domain, and uncertain domain, and the three-way attribute random selection rules are designed to randomly select the feature attributes that conform to the rules from different domains. Secondly, the classifier evaluation function is constructed by combining pure accuracy and diversity, and the anomaly traffic detection base classifier with a high evaluation value is selected for integration to eliminate the unstable factors caused by randomness in the process of base classifier generation. Thirdly, the optimal node weight combination of the base classifier is obtained by iterative calculation of the gray wolf optimization algorithm to further improve the prediction effect and robustness of the model. Finally, the model is applied to the abnormal traffic detection dataset. The experimental results show that the prediction accuracy of the three-way selection random forest optimization model on CIC-IDS2017, KDDCUP99, and NSLKDD datasets is 96.1%, 95.2%, and 95.3%, respectively, which has a better detection effect than other machine learning algorithms.

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Three-way selection random forest algorithm based on decision boundary entropy

Cited by 8 publications

References 40 publications

SOINN Intrusion Detection Model Based on Three-Way Attribute Reduction

SOINN Intrusion Detection Model Based on Three-Way Attribute Reduction

Research on Development Strategy of Ethnic Sports Tourism Resources Based on Stochastic Forest Algorithm

Three-Way Selection Random Forest Optimization Model for Anomaly Traffic Detection

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