A new fast reduction technique based on binary nearest neighbor tree

Li, Juan; Wang, Yuping

doi:10.1016/j.neucom.2014.08.028

Cited by 17 publications

(22 citation statements)

References 25 publications

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“…In the process of sorting instances, although the influence of all instances of different classes is considered, it is regarded as adverse information. BNNT algorithm uses the binary nearest neighbor tree to select the instance [8]. e algorithm only considers the k nearest neighbor instances of the selected instance and does not consider the influence of remaining instances.…”

Section: Instance Selection Techniquementioning

confidence: 99%

“…In recent years, many instance selection techniques have been proposed to improve the performance of IDS [7][8][9][10][11][12][13][14][15]. However, in terms of the factors and application areas of instance selection, there are mainly four problems in existing instance selection algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, only the influence of a portion of instances is taken into account for selecting the instance [7][8][9]. For instance, the instance selection algorithm based on partition and cluster center (PCIS) [7] selects representative instance by considering K nearest neighbor instances of the same class; e binary nearest neighbor tree algorithm (BNNT) [8] and the constraint nearest neighbor-based instance reduction algorithm (CNNIR) [9] select representative instance by K nearest neighbor instances of the selected instance. As these instance selection algorithms only consider the influence of a portion of instances and ignore the influence of remaining instances, some important instances are not selected.…”

Section: Introductionmentioning

confidence: 99%

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Representativeness-Based Instance Selection for Intrusion Detection

Fei

Yang

Zhang

et al. 2021

Security and Communication Networks

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With the continuous development of network technology, an intrusion detection system needs to face detection efficiency and storage requirement when dealing with large data. A reasonable way of alleviating this problem is instance selection, which can reduce the storage space and improve intrusion detection efficiency by selecting representative instances. An instance is representative not only in its class but also in different classes. This representativeness reflects the importance of an instance. Since the existing instance selection algorithm does not take into account the above situations, some selected instances are redundant and some important instances are removed, increasing storage space and reducing efficiency. Therefore, a new representativeness of instance is proposed and considers not only the influence of all instances of the same class on the selected instance but also the influence of instances of different classes on the selected instance. Moreover, it considers the influence of instances of different classes as an advantageous factor. Based on this representativeness, two instance selection algorithms are proposed to handle balanced and imbalanced data problems for intrusion detection. One is a representative-based instance selection for balanced data, which is named RBIS and selects the same proportion of instances from each class. The other is a representative-based instance selection for imbalanced data, which is named RBIS-IM and selects important majority instances according to the number of instances of the minority class. Compared with other algorithms on the benchmark data sets of intrusion detection, experimental results verify the effectiveness of the proposed RBIS and RBIS-IM algorithms and demonstrate that the proposed algorithms can achieve a better balance between accuracy and reduction rate or between balanced accuracy and reduction rate.

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Section: Instance Selection Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Representativeness-Based Instance Selection for Intrusion Detection

Fei

Yang

Zhang

et al. 2021

Security and Communication Networks

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“…The earliest edition method is ENN proposed by Wilson [14]. Many hybrid methods, such as the ATISA [18] (adaptive threshold-based instance selection algorithm), the BNNT (binary nearest neighbor tree) [19] and the IRB (instanceRank based on borders) [21], use the ENN as a step to filter out noisy instances. Despite the excellent performance of ENN, it is oversimple.…”

Section: Related Workmentioning

confidence: 99%

“…Other variants of ENN include the 2 all-KNN (ALL-KNN) [15], the repeated ENN (RENN) [15] and the modified ENN (MENN) [16]. Hybrid methods, which combine the characteristics of edition methods and condensation methods, also have been widely used for the time being [17][18][19][20][21][22]. The edition method is our focus in this paper.…”

Section: Introductionmentioning

confidence: 99%

ELS: A Fast Parameter-Free Edition Algorithm With Natural Neighbors-Based Local Sets for k Nearest Neighbor

Zhao

2020

IEEE Access

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The instance reduction is one of the data preprocessing methods and aims to remove noises and (or) redundant instances from the training set. In the instance reduction, one of the most representative techniques is the edition method which can remove harmful instances from the training set to improve the prediction accuracy of k nearest neighbor (KNN). Nevertheless, most of existing edition methods still have some drawbacks, such as the parameter dependency, high computational time and relatively low accuracy. To solve these problems, we present a new fast parameter-free edition method based on local sets with natural neighbors (ELS). In ELS, we define a new concept of local sets by introducing natural neighbors. ELS can use the local sets to keep more reasonable class boundaries and effectively filtering out noisy instances (including global outliers). The main advantages of ELS are that (a) it is parameter-free; (b) it can remove global outliers and noisy instances; (c) it is relatively fast. Experiments clearly verify that (a) ELS outperforms existing representative edition methods in improving the prediction accuracy of KNN; (b) ELS can improve the performance of the condensation method and hybrid method in terms of both accuracy and reduction; (c) ELS consumes relatively low running time.

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An Adaptive Algorithm of Showing Intuitively the Structure of a Binary Tree

Ye¹,

Huang

2019

Advances in Intelligent Systems and Computing

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A new fast reduction technique based on binary nearest neighbor tree

Cited by 17 publications

References 25 publications

Representativeness-Based Instance Selection for Intrusion Detection

Representativeness-Based Instance Selection for Intrusion Detection

ELS: A Fast Parameter-Free Edition Algorithm With Natural Neighbors-Based Local Sets for k Nearest Neighbor

An Adaptive Algorithm of Showing Intuitively the Structure of a Binary Tree

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