The Implementation of Subspace Outlier Detection in K-Nearest Neighbors to Improve Accuracy in Bank Marketing Data

2020 ◽  
Vol 8 (2) ◽  
pp. 545-550
Author(s):  
Dimas Aryo Anggoro
Keyword(s):  
Outlier Detection ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Improve Accuracy ◽  
Marketing Data ◽  
Bank Marketing

K-NN Based Outlier Detection Technique on Intrusion Dataset

2017 ◽  
Vol 7 (1) ◽  
pp. 58-70 ◽  
Author(s):  
Santosh Kumar Sahu ◽  
Sanjay Kumar Jena ◽  
Manish Verma
Keyword(s):  
Outlier Detection ◽  
Time Complexity ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
The Other ◽  
Detection Technique ◽  
K Nearest Neighbors ◽  
Time Required ◽  
Local Outlier ◽  
Quick Sort

Outliers in the database are the objects that deviate from the rest of the dataset by some measure. The Nearest Neighbor Outlier Factor is considering to measure the degree of outlier-ness of the object in the dataset. Unlike the other methods like Local Outlier Factor, this approach shows the interest of a point from both neighbors and reverse neighbors, and after that, an object comes into consideration. We have observed that in GBBK algorithm that based on K-NN, used quick sort to find k nearest neighbors that take O (N log N) time. However, in proposed method, the time required for searching on K times which complete in O (KN) time to find k nearest neighbors (k < < log N). As a result, the proposed method improves the time complexity. The NSL-KDD and Fisher iris dataset is used, and experimental results compared with the GBBK method. The result is same in both the methods, but the proposed method takes less time for computation.

A Data Stream Outlier Detection Algorithm Based on Reverse K Nearest Neighbors

2011 ◽  
Vol 225-226 ◽  
pp. 1032-1035 ◽  
Author(s):  
Zhong Ping Zhang ◽  
Yong Xin Liang
Keyword(s):  
Outlier Detection ◽  
Data Stream ◽  
Concept Drift ◽  
Real Data ◽  
Nearest Neighbors ◽  
Detection Algorithm ◽  
Data Sets ◽  
K Nearest Neighbors ◽  
Query Manager ◽  
Current Window

This paper proposes a new data stream outlier detection algorithm SODRNN based on reverse nearest neighbors. We deal with the sliding window model, where outlier queries are performed in order to detect anomalies in the current window. The update of insertion or deletion only needs one scan of the current window, which improves efficiency. The capability of queries at arbitrary time on the whole current window is achieved by Query Manager Procedure, which can capture the phenomenon of concept drift of data stream in time. Results of experiments conducted on both synthetic and real data sets show that SODRNN algorithm is both effective and efficient.

scholarly journals Tropical Balls and Its Applications to K Nearest Neighbor over the Space of Phylogenetic Trees

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 779
Author(s):  
Ruriko Yoshida
Keyword(s):  
Supervised Learning ◽  
Phylogenetic Trees ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
Learning Method ◽  
Dimensional Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

A tropical ball is a ball defined by the tropical metric over the tropical projective torus. In this paper we show several properties of tropical balls over the tropical projective torus and also over the space of phylogenetic trees with a given set of leaf labels. Then we discuss its application to the K nearest neighbors (KNN) algorithm, a supervised learning method used to classify a high-dimensional vector into given categories by looking at a ball centered at the vector, which contains K vectors in the space.

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