NearCount: Selecting critical instances based on the cited counts of nearest neighbors

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NearCount: Selecting critical instances based on the cited counts of nearest neighbors

机译：NearCount：根据引用的最近邻居计数选择关键实例

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Traditional instance selection algorithms are not good at addressing imbalanced problems. Moreover, most of them are sensitive to noise instances and suffer from complex selection rules. To solve these problems, in this paper, we propose a concise learning framework named NearCount to determine the importance of the instance without editing noise. In NearCount, the importance of an instance corresponds to the cited counts. The count is determined by the number of times that one instance is selected as a nearest neighbor of instances in different classes. For the instances with nonzero cited counts, the importance of the instance is inversely proportional to the cited count. To handle classification problems with different data distributions, two detailed NearCount-based algorithms - NearCount-IM and NearCount-IS - are introduced. For imbalanced problems, NearCount-IM selects the important majority instances with an equal number of minority instances, thus balancing the data distribution. For balanced scenarios, NearCount-IS selects the instances whose cited counts are greater than zero and equal or less than the number of nearest neighbors as critical instances in every class. The proposed NearCount-IM and NearCount-IS algorithms are evaluated by comparing them with classical instance selection algorithms on the benchmark data sets. Experiments validate the effectiveness of the proposed algorithms. (C) 2019 Elsevier B.V. All rights reserved.

机译：传统的实例选择算法不能很好地解决不平衡问题。而且，它们中的大多数对噪声实例敏感，并且受复杂的选择规则的影响。为了解决这些问题，在本文中，我们提出了一个名为NearCount的简洁学习框架，用于确定实例的重要性而无需编辑噪声。在NearCount中，实例的重要性与引用的计数相对应。该计数由一个实例被选择为不同类别中实例的最近邻居的次数确定。对于引用计数非零的实例，实例的重要性与引用计数成反比。为了处理具有不同数据分布的分类问题，引入了两种详细的基于NearCount的算法-NearCount-IM和NearCount-IS。对于不平衡问题，NearCount-IM选择具有相同数量的少数实例的重要多数实例，从而平衡数据分布。对于平衡方案，NearCount-IS会选择引用计数大于零且等于或小于最近邻居数的实例作为每个类中的关键实例。通过将它们与基准数据集上的经典实例选择算法进行比较，可以评估所提议的NearCount-IM和NearCount-IS算法。实验验证了所提出算法的有效性。（C）2019 Elsevier B.V.保留所有权利。

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《Knowledge-Based Systems》 |2020年第29期|105196.1-105196.17|共17页
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East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China|East China Univ Sci & Technol Dept Comp Sci & Engn Shanghai 200237 Peoples R China;

East China Univ Sci & Technol Dept Comp Sci & Engn Shanghai 200237 Peoples R China;

East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China;

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正文语种 eng
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关键词
Critical instance; Nearest neighbor; Cited counts; Imbalanced problem; Instance selection;

机译：关键实例;最近的邻居;引用计数;不平衡的问题;实例选择;

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专利

1. Integrating Instance Selection, Instance Weighting, and Feature Weighting for Nearest Neighbor Classifiers by Coevolutionary Algorithms [J] . Derrac J., Triguero I., Garcia S., Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on . 2012,第5期

机译：通过协进化算法集成最近邻分类器的实例选择，实例加权和特征加权
2. Penalty-Reward Based Instance Selection Method in Cloud Environment Using the Concept of Nearest Neighbor [J] . Partha Ghosh, Akash Saha, Santanu Phadikar Procedia Computer Science . 2016,第1期

机译：最近邻概念的云环境中基于罚分的实例选择方法
3. A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer [J] . Onan Aytug Expert Systems with Application . 2015,第20期

机译：模糊粗糙最近邻分类器结合基于一致性的子集评估和实例选择，用于乳腺癌的自动诊断
4. A Co-evolutionary Framework for Nearest Neighbor Enhancement: Combining Instance and Feature Weighting with Instance Selection [C] . Joaquin Derrac, Isaac Triguero, Salvador Garcia, International conference on hybrid artificial intelligent systems;HAIS 2012;Session on systems, man, and cybernetics by HAIS;Session on methods of classifier fusion;Session on HAIS for computer security;Session on data mining: data preparation and analysis;Session on hybrid artificial intelligence systems in management of production systems;Session on hybrid artificial intelligent systems for ordinal regression;Session on hybrid computational intelligence and lattice computing for image and signal processing;Session on hybrid metaheuristics for combinatorial optimization and modelling complex systems;Workshop on nonstationary models of pattern recognition and classifier combinations . 2012

机译：最近邻增强的共同进化框架：将实例和特征加权与实例选择相结合
5. Voting Nearest Neighbors: SVM Constraints Selection Algorithm Based on K-Nearest Neighbors [D] . Moreira da Costa, Leandro. 2019

机译：投票最近的邻居：基于K-Indect邻居的SVM约束选择算法
6. An Effective Singular Value Selection and Bearing Fault Signal Filtering Diagnosis Method Based on False Nearest Neighbors and Statistical Information Criteria [O] . Zhiqiang Liao, Liuyang Song, Peng Chen, 2018

机译：基于虚假最近邻和统计信息准则的有效奇异值选择和轴承故障信号滤波诊断方法
7. Penalty-Reward Based Instance Selection Method in Cloud Environment Using the Concept of Nearest Neighbor [O] . Ghosh Partha, Saha Akash, Phadikar Santanu 2016

机译：最近邻概念的云环境中基于罚分的实例选择方法
8. Nearest Neighbor Averaging and its Effect on the Critical Level and Minium Detectable Concentration for Scanning Radiological Survey Instruments that Perform Facility Release Surveys. [R] . Fournier, S. D., Beall, P. S., Miller, M. L. 2014

机译：最近邻平均值及其对执行设施释放调查的扫描放射测量仪器的临界水平和可检测浓度的影响。

NearCount: Selecting critical instances based on the cited counts of nearest neighbors

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