A New Classification Algorithm Using Mutual Nearest Neighbors

Liu, Huawen; Zhang, Shichao; Zhao, Jianming; Zhao, Xiangfu; Mo, Yuchang

doi:10.1109/gcc.2010.23

Cited by 28 publications

(21 citation statements)

References 14 publications

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“…Figure 16 compares the AUC results of iNNE with LOF with different proportions of data size for training on the 3 data sets. [32][33][34] The result in Figure 16 shows that iNNE obtains a stable AUC on the 3 data sets regardless of the training data size.…”

Section: Performance On Benchmark Data Setsmentioning

confidence: 99%

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Isolation‐based anomaly detection using nearest‐neighbor ensembles

Bandaragoda

Ting

Albrecht

et al. 2018

Computational Intelligence

113

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The first successful isolation-based anomaly detector, ie, iForest, uses trees as a means to perform isolation. Although it has been shown to have advantages over existing anomaly detectors, we have identified 4 weaknesses, ie, its inability to detect local anomalies, anomalies with a high percentage of irrelevant attributes, anomalies that are masked by axis-parallel clusters, and anomalies in multimodal data sets. To overcome these weaknesses, this paper shows that an alternative isolation mechanism is required and thus presents iNNE or isolation using Nearest Neighbor Ensemble.Although relying on nearest neighbors, iNNE runs significantly faster than the existing nearest neighbor-based methods such as the local outlier factor, especially in data sets having thousands of dimensions or millions of instances. This is because the proposed method has linear time complexity and constant space complexity. KEYWORDSanomaly detection, ensemble learning, isolation-based, nearest neighbor, outlier detection INTRODUCTIONAnomaly detection is an important data mining task that has a diverse range of applications in various domains. 1,2 The explosive growth of databases in both size and dimensionality is challenging for anomaly detection methods in two important aspects: the requirement of low computational 968 /journal/coin Computational Intelligence. 2018;34:968-998. BANDARAGODA ET AL. 969cost and the susceptibility to issues in high-dimensional data sets. Efficient methods are required in time-critical applications such as network intrusion detection and credit card fraud detection. However, the time complexity of most existing methods is on the order of O(n 2 ) (where n is the data set size), which is prohibitively expensive for large data sets. Therefore, efficient and scalable methods for large data sets are highly desirable.iForest 3 is a unique anomaly detector because it utilizes an isolation mechanism to detect anomalies. iForest isolates each instance from the rest of the instances through recursive axis-parallel subdivisions. Those instances that can be easily isolated are likely to be anomalies.The key advantage of iForest is its linear execution time, which makes it extremely efficient in comparison to other methods, and thus, it is a very attractive option for large data sets. iForest has been shown 3,4 to have better detection accuracy and faster runtime than many state-of-the-art methods including the local outlier factor (LOF) 5 and optimal reciprocal collision avoidance. 6 Despite these advantages, our investigation finds that the current isolation mechanism has weaknesses in detecting the following 4 types of anomalies.1. Local anomalies: iForest uses a global anomaly score that is not sensitive to the local data distribution of a data set. 2. Anomalies with low relevant dimensions: In high-dimensional data, iForest can only utilize a subset of the dimensions to create isolation trees. Each subset does not usually contain sufficient relevant dimensions to detect anomalies when the number of relevant dimens...

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Section: Performance On Benchmark Data Setsmentioning

confidence: 99%

“…This is because k for k-NN-based algorithms usually needs to be adjusted for different data sizes. [32][33][34]…”

Section: Performance On Benchmark Data Setsmentioning

confidence: 99%

Isolation‐based anomaly detection using nearest‐neighbor ensembles

Bandaragoda

Ting

Albrecht

et al. 2018

Computational Intelligence

113

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show abstract

“…When the null-hypothesis is rejected by the Friedmans test, at 95% of confidence level, we can proceed with a post-hoc test to detect which differences among the methods are significant [5]. We ran the Nemenyis multiple comparison test, comparing all algorithms, where the Nemenyi test points out whether there is a significant difference between the algorithms involved in the experimental evaluation, whenever their average rank differs by at least the critical difference 6 . In all cases, the null-hypothesis was rejected by the Friedman test and the Nemenyi test was used.…”

Section: Resultsmentioning

confidence: 99%

“…In this work, we propose the use of the following two other strategies, which have already been used for single-label learning [6], but not for multi-label learning, to identify the set of these examples:…”

Section: Proposed Algorithmsmentioning

confidence: 99%

Lazy Multi-label Learning Algorithms Based on Mutuality Strategies

Cherman

Spolaôr

Valverde-Rebaza

et al. 2014

J Intell Robot Syst

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Lazy multi-label learning algorithms have become an important research topic within the multilabel community. These algorithms usually consider the set of standard k-Nearest Neighbors of a new instance to predict its labels (multi-label). The prediction is made by following a voting criteria within the multi-labels of the set of k-Nearest Neighbors of the new instance. This work proposes the use of two alternative strategies to identify the set of these examples: the Mutual and Not Mutual Nearest Neighbors rules, which have already been used by lazy single-learning algorithms. In this work, we use these strategies to extend the lazy multi-label algorithm BRkNN. An experimental evaluation carried out to compare both mutuality strategies with the original BRkNN algorithm and the well-known MLkNN lazy algorithm on 15 benchmark datasets showed that MLkNN presented the best predictive performance E. A. Cherman ( ) · N. Spolaôr · J. Valverde-Rebaza ·

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“…No entanto, a eficiência desses algoritmos pode ser melhorada utilizando recursos como estruturas de dados especiais (Liu et al, 2010 MMP O Multi-class Multi-label Perceptron, proposto por Crammer et al (2003), é um algoritmo da família dos ranqueadores de rótulos baseado no perceptron (Haykin, 1998;Minsky e Papert, 1969). O MMP cria um perceptron para cada rótulo, mas a atualização dos pesos para cada perceptron é realizada de tal maneira que se obtenha um perfeito ranqueamento de todos os rótulos.…”

Section: Adaboost Multirrótulounclassified

Aprendizado de máquina multirrótulo: explorando a dependência de rótulos e o aprendizado ativo

Cherman¹

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A New Classification Algorithm Using Mutual Nearest Neighbors

Cited by 28 publications

References 14 publications

Isolation‐based anomaly detection using nearest‐neighbor ensembles

Isolation‐based anomaly detection using nearest‐neighbor ensembles

Lazy Multi-label Learning Algorithms Based on Mutuality Strategies

Aprendizado de máquina multirrótulo: explorando a dependência de rótulos e o aprendizado ativo

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