Weigted-KNN and its application on UCI

Li, Zhang; Zhang, Chengjin; Xu, Qingyang; Liu, Chunfa

doi:10.1109/icinfa.2015.7279570

Cited by 11 publications

(7 citation statements)

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“…KNN is also known as a lazy learner because in its training phase it simply stores all the tuples or do less calculation on the training dataset and awaits until a test tuple is given to it [1] [4].When a test tuple is given, it calculates the similarity between the test tuple and all the training tuples by using distance metrics. the best distance metrics are used like Manhattan distance, Minkowski distance, Mahalanobis distance, Chebyshev distance, and the most commonly used distance metric used in KNN is Euclidean distance metric which gives better results as compared to other distance metrics [5] [6]. The Euclidean distance metric is used when the attributes are not strongly correlated [5] [6].…”

Section: Introductionmentioning

confidence: 99%

“…the best distance metrics are used like Manhattan distance, Minkowski distance, Mahalanobis distance, Chebyshev distance, and the most commonly used distance metric used in KNN is Euclidean distance metric which gives better results as compared to other distance metrics [5] [6]. The Euclidean distance metric is used when the attributes are not strongly correlated [5] [6]. Let x is a sample whose class label is unknown and y is a set of n training tuples [7] then the Euclidean distance ( , ) is calculated using the formula :…”

Section: Introductionmentioning

confidence: 99%

“…Some key advantages of KNN are: (1) very simple to use, (2) it takes very small time in its training phase, (3) easy to implement and to understand, (4) gives effective results in large datasets as compared to other algorithms, (5) it is independent of data distributions (6) it is robust to noisy data; (7) Effective results when training data is large.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Performance of Dual Weighted K- Nearest Neighbor Classifier

Rani¹

2018

IJCA

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A non-parametric, very simple to use, effective instance-based learning algorithm called K-Nearest Neighbor (KNN), is most widely used to classify the objects in data mining. KNN has some shortcomings which affect its classification performance like the equal impact of all attributes, curse of dimensionality, the value of "k' parameter and simple voting. A variety of techniques are developed in literature to get better performance. This paper presents an improved algorithm called dual weighted KNN that is a combination of attribute weighted and instance weighted techniques. To verify the performance of proposed algorithm it is conducted on fourteen different datasets taken from UCI in "R" data mining tool. The results show that the proposed algorithm significantly outperforms than traditional KNN, Attribute weighted KNN and Instance weighted KNN.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating the Performance of Dual Weighted K- Nearest Neighbor Classifier

Rani¹

2018

IJCA

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“…Li et al proposed an attribute weighting method where the unrelated attributes are reduced and weights are assign to each attribute by using the method of sensitivity which increase the efficiency of algorithm [15]. A new algorithm based on dynamic weighting to enhance the classification accuracy of the KNN algorithm was devised by K Maryam.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

An Appraise of KNN to the Perfection

Rani¹,

Vashishtha²

2017

IJCA

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K-Nearest Neighbor (KNN) is highly efficient classification algorithm due to its key features like: very easy to use, requires low training time, robust to noisy training data, easy to implement. However, it also has some shortcomings like high computational complexity, large memory requirement for large training datasets, curse of dimensionality and equal weights given to all attributes. Many researchers have suggested various advancements and improvements in KNN to overcome these shortcomings. This paper appraising various advancements and improvements in KNN.

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“…Xiao and Ding suggested a weighting method based on weighted entropy of attribute value which enhance the accuracy of classification [16]. Li et al proposed an attribute weighting method where the irrelevant attributes are reduced and weight is assigned by the method of sensitivity which improves the efficiency of algorithm [17]. A Distanceweighted K-Nearest-Neighbor rule developed by Lan Du et al using the dual distance-weighted function [18].…”

confidence: 99%

A Review of various KNN Techniques

Rani¹

2017

IJRASET

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K-Nearest Neighbor is highly efficient classification algorithm due to its key features like: very easy to use, requires low training time, robust to noisy training data, easy to implement, but alike other algorithms it also has some shortcomings as computation complexity, large memory requirement for large training datasets, curse of dimensionality which can't be ignored and it also gives equal weights to all attributes. Many researchers have developed various techniques to overcome these shortcomings. In this paper such techniques are discussed. Some of these techniques are structure based, like R-Tree, R*-Tree, Voronoi cells, Branch & Bound algorithm and these algorithms overcome computation complexity as well as also help to reduce the searching time for neighbors in multimedia training datasets. Some techniques are non-structure based like Weighted KNN, Model based KNN, distance based KNN, Class confidence weighted KNN, Dynamic weighted KNN, Clustering based KNN, and Pre-classification based KNN, and these algorithms reduce memory limitation, curse of dimensionality and time complexity.

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Weigted-KNN and its application on UCI

Cited by 11 publications

References 1 publication

Evaluating the Performance of Dual Weighted K- Nearest Neighbor Classifier

Evaluating the Performance of Dual Weighted K- Nearest Neighbor Classifier

An Appraise of KNN to the Perfection

A Review of various KNN Techniques

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