Analysis of the Nearest Neighbor Classifiers: A Review

Agarwal, Yash; Poornalatha, G

doi:10.1007/978-981-15-3514-7_43

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…A K-Nearest Neighbor (KNN) algorithm is a data classification method that estimates the likelihood that a data point will become a member of one group or another, depending on which group the nearest data point belongs. It means a KNN classifier is a distance function, in effect an application of the Pythagorean theorem, which measures the difference or similarity between two instances [11]. The KNN ML approach is practical in several network categories (e.g., IoT, industry WLAN IoT, and self-designed networks) where each network could be affected by different attacks.…”

Section: Improve Diversity Of Generated Dtmentioning

confidence: 99%

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

et al. 2022

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Despite its technological benefits, Internet of Things (IoT) has cyber weaknesses due to the vulnerabilities in the wireless medium. Machine learning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited surveys to fully investigate APT attacks in IoT networks due to the lack of public datasets with all types of APT attacks. It is worth to bridge the state-of-the-art in network attack detection with APT attack detection in a comprehensive review article. This survey article reviews the security challenges in IoT networks and presents the well-known attacks, APT attacks, and threat models in IoT systems. Meanwhile, signature-based, anomaly-based, and hybrid IDSs are summarized for IoT networks. The article highlights statistical insights regarding frequently applied ML-based methods against network intrusion. Finally, open issues and challenges for common network intrusion and APT attacks are presented for future research.

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Section: Improve Diversity Of Generated Dtmentioning

confidence: 99%

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

et al. 2022

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“…The kNN algorithm compares the unknown sample to the k training sample, which is the new sample's nearest neighbor. Preliminary theoretical results were published by [18], while a thorough summary was published by [19]. Finding the k closest training examples was the first step in applying the kNN algorithm to a new instance.…”

Section: K-nearest Neighbors (Knn)mentioning

confidence: 99%

Smote vs. Random Undersampling for Imbalanced Data - Car Ownership Demand Model

Chaipanha¹,

Kaewwichian²

2022

Komunikácie

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Because the numbers of cars reflect each person's travel behaviors for each specific location, the car ownership demand model plays a dominant role in analysis of the travel demand in order to understand each area's individual and household travel behaviors. However, the study project for the master plan of the Khon Kaen expressway represented imbalanced data; namely, the majority class and the minority class were not equal. Before developing a machine learning model, this study suggested a solution to balance the data by using oversampling and under-sampling techniques. The data, which had been improved with SMOTE (Synthetic Minority Oversampling Technique) and kNN (k-nearest neighbors) (k = 5), demonstrated a better effect than the other algorithms that were studied. The TPR (true positive rate) for the rural and suburban areas, which are types of regions with very different imbalance ratios, was calculated before balancing the data at 46.9 % and 46.4 %. As a result, the TPR values were 63.5 % and 54.4 %, respectively, following the data balancing.

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“…The kNN algorithm compares the unknown sample with the k training sample, the closest neighbor of the new sample. The preliminary theoretical results can be found in [27], and a comprehensive overview can be found in [28] In the next step, the kNN algorithm classifies the unknown sample by voting on the majority of the neighbors it finds. In the case of a regression, the predicted value is the average of the found values of the neighbor.…”

Section: K-nearest Neighbors (Knn)mentioning

confidence: 99%

Multiclass Classification with Imbalanced Datasets for Car Ownership Demand Model – Cost-Sensitive Learning

Kaewwichian

2021

PROMET

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In terms of the travel demand prediction from the household car ownership model, if the imbalanced data were used to support the transportation policy via a machine learning model, it would negatively affect the algorithm training process. The data on household car ownership obtained from the study project for the expressway preparation in the Khon Kaen Province (2015) was an unbalanced dataset. In other words, the number of members of the minority class is lower than the rest of the answer classes. The result is a bias in data classification. Consequently, this research suggested balancing the datasets with cost-sensitive learning methods, including decision trees, k-nearest neighbors (kNN), and naive Bayes algorithms. Before creating the 3-class model, a k-folds cross-validation method was applied to classify the datasets to define true positive rate (TPR) for the model’s performance validation. The outcome indicated that the kNN algorithm demonstrated the best performance for the minority class data prediction compared to other algorithms. It provides TPR for rural and suburban area types, which are region types with very different imbalance ratios, before balancing the data of 46.9% and 46.4%. After balancing the data (MCN1), TPR values were 84.4% and 81.4%, respectively.

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Analysis of the Nearest Neighbor Classifiers: A Review

Cited by 6 publications

References 24 publications

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

Smote vs. Random Undersampling for Imbalanced Data - Car Ownership Demand Model

Multiclass Classification with Imbalanced Datasets for Car Ownership Demand Model – Cost-Sensitive Learning

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