Sparse Coefficient-Based ${k}$ -Nearest Neighbor Classification

Ma, Hongwen; Gou, Jianping; Wang, Xili; Jia, Ke; Zeng, Shaoning

doi:10.1109/access.2017.2739807

Cited by 48 publications

(20 citation statements)

References 40 publications

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“…In pattern recognition, the sensitivity to k is the key issue of kNN classifiers. To select high-quality kNNs, [19] proposes a kNN-based classifier based on the sparse coefficients which can well reflect the neighborhood structure of data, and [20] proposes a generalized mean distance-based kNN classifier, where the k local mean vectors per class are calculated and they can represent the local sample distributions of each class.…”

Section: ) Other Related Workmentioning

confidence: 99%

Direction-Aware Nearest Neighbor Query

Guo

Yang

2019

IEEE Access

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Although the direction aspect is as important as the distance aspect when retrieving spatial objects, the studies of direction-aware queries are limited. The direction-based surrounder query cannot guarantee good directional diversity. In this paper, we propose the direction-aware nearest neighbor query (DNN query), which can recommend the nearest objects and ensure that the results have good directional diversity. Given a query point q and an angular threshold θ, the DNN query searches for the nearest neighbors on various directions around q. The DNN query can apply to the scenario where the user searches for the nearest objects around him. It can also apply to the scenario where the user searches for the photos of a geographic object according to the locations where the photos are taken. It can find photos that capture different views of the query object. In this paper, we propose the point-DNN query, where the query object is a point, and extend it to the range-DNN query, where the query object is a rectangle. We design algorithms to answer the DNN queries and also conduct comprehensive experiments to evaluate the performances of the algorithms. INDEX TERMS Nearest neighbor query, direction-aware, spatial database.

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Section: ) Other Related Workmentioning

confidence: 99%

Direction-Aware Nearest Neighbor Query

Guo

Yang

2019

IEEE Access

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“…One part is used for implementing the approach and other art is used for testing the approach it is called testing data. KNN is only applicable for numeric data [10]. Based on the distribution of NSL-KDD dataset, It is found that categorical fields like protocol plays major role in classification.…”

Section: Protocol Specific Intrusion Detection System Using Knn Cmentioning

confidence: 99%

Protocol-Specific Intrusion Detection System using KNN Classifier

Sameera¹

2018

IJRASET

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Information Security Analytics is evolving as a big trend in recent years. Lots of existing knowledge is not enough to understand it completely. In order to explore it more and to sharpen research work on it, first of all, user has to understand the importance of security. The target is on analytics which is the process of taking raw data and by processing the data and produces meaningful information through which one can derive patterns. Information security requires enthusiastic people who are interested in taking security challenges against continually evolving attacks, as opportunities to excel in the field of security. In this digital world, attacker's strategy keeps changing. They even can make use of defenders actions as a step to build a new attack. This paper proposes a new approach of applying called "protocol-specific Intrusion Detection System Using KNN Classifier" for identifying an abnormal/anomalous transaction, which leads to an attack. This can be done by submitting each observation to the model, which is already trained with some training data based on the protocol of the transaction.

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“…Liu and Zhang [18] proposed a scheme reconstructing points of the test dataset by learning the correlation matrix, in which different k values are assigned to different points of test data based on the training data. In addition, Ma et al [19] proposed a coefficientweighted KNN classifier and a residual-weighted KNN classifier for making classification decisions on the basis of sparse coefficients in the sparse representation. Gou et al [20] proposed the two-phase probabilistic collaborative representation-based classification (TPCRC) to enhance the power of pattern discrimination in PCRC.…”

Section: Introductionmentioning

confidence: 99%

An Improved KNN‐Based Slope Stability Prediction Model

Huang

Lyu

2020

Advances in Civil Engineering

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An accurate slope prediction model is important for slope reinforcement before the disaster. The k-nearest neighbor (KNN) algorithm, as a simple and effective nonparametric machine learning method, is widely applied in classification recognition. In our study, the k-nearest neighbor (KNN) algorithm is improved to reduce its sample dependence and improve the robustness of the algorithm, and then the prediction model of the slope stability is proposed based on the improved k-nearest neighbor (KNN) algorithm. Extensive experimental results show that our proposed prediction model achieves high prediction performance in this regard. Moreover, a comparison between our proposed prediction model and the finite element method, which is the classical theoretical method of slope stability, was made, which will provide an important approach to predicting the slope stability for slope engineering. Finally, shaking table test of a slope model is conducted to evaluate whether the slope is stable or not, and the experimental results are in good agreement with the prediction results of our proposed prediction model, which further demonstrates its effectiveness.

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Sparse Coefficient-Based ${k}$ -Nearest Neighbor Classification

Cited by 48 publications

References 40 publications

Direction-Aware Nearest Neighbor Query

Direction-Aware Nearest Neighbor Query

Protocol-Specific Intrusion Detection System using KNN Classifier

An Improved KNN‐Based Slope Stability Prediction Model

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