An Enhanced K-Nearest Neighbor Algorithm Using Information Gain and Clustering

Taneja, Shweta; Gupta, Charu; Goyal, Kratika; Gureja, Dharna

doi:10.1109/acct.2014.22

Cited by 69 publications

(29 citation statements)

References 7 publications

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“…In the KNN classification, as training elements belonging to different classes become distant from the test element, the distances of neighbours instead of their number become important [18]. Because the number of training elements belonging to different class very close to the test element decreases.…”

Section: Resultsmentioning

confidence: 99%

Density Weighted K-Nearest Neighbors Algorithm for Outliers in the Training Set Are So Close to the Test Element

Akben¹,

Alkan²

2015

JEE

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Abstract:In KNN (K-Nearest Neighbour) method, the distance-weighted algorithm is applied in order to reduce the effect of noisy data. However, as this weighting algorithm will be insufficient when the outliers in the training set are so close to the test element, a new weighting algorithm is required for KNN. In this study, instead of distance-weighting, a new density-weighted KNN algorithm is proposed for reducing the effect of noisy data. In the first stage of the proposed method, the coefficient of density of each element in the training set was obtained by Parzen window method. And then, the membership of each test element was determined according to the total of density coefficients (weights) of neighbours belonging to the same class. As for the last stage, the performance results of the frequently used KNN methods and the proposed method (Density-weighted KNN, Classical KNN and Distance-weighted KNN) were compared. The obtained results have shown that the proposed method is more successful by almost 1% than classical KNN method, by 9% than distance weighted KNN method. Moreover, it is more successful than other KNN methods when the test element is so close to the training set elements.

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Section: Resultsmentioning

confidence: 99%

Density Weighted K-Nearest Neighbors Algorithm for Outliers in the Training Set Are So Close to the Test Element

Akben¹,

Alkan²

2015

JEE

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“…The classification of musical genres is a field that has always been of great interest in the scientific community in the last times with the application of supervised machine learning techniques, such as Gaussian Mixture model [1] and k-nearest neighbour classifiers [2]. There have been several works that seek to refine more and more the methods to obtain better classifiers.…”

Section: Related Workmentioning

confidence: 99%

Automatic Identification of Traditional Colombian Music Genres Based on Audio Content Analysis and Machine Learning Techniques

Cruz

Cristancho

Camargo

2019

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Colombia has a diversity of genres in traditional music, which allows to express the richness of the Colombian culture according to the region. This musical diversity is the result of a mixture of African, native Indigenous, and European influences. Organizing large collections of songs is a time consuming task that requires that a human listens to fragments of audio to identify genre, singer, year, instruments and other relevant characteristics that allow to index the song dataset. This paper presents a method to automatically identify the genre of a Colombian song by means of its audio content. The method extracts audio features that are used to train a machine learning model that learns to classify the genre. The method was evaluated in a dataset of 180 musical pieces belonging to six folkloric Colombian music genres: Bambuco, Carranga, Cumbia, Joropo, Pasillo, and Vallenato. Results show that it is possible to automatically identify the music genre in spite of the complexity of Colombian rhythms reaching an average accuracy of 69%.

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“…A new method called Hybrid dynamic k-nearest-neighbor, distance and attribute weighted for classification was devised by Jia Wu et al [23]. KNN algorithm based on information entropy weighting of attribute was proposed by Shweta Taneja et al [24] . It improves the accuracy of classification but spent more time in classification when many categorical attributes are given.…”

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.

show abstract

An Enhanced K-Nearest Neighbor Algorithm Using Information Gain and Clustering

Cited by 69 publications

References 7 publications

Density Weighted K-Nearest Neighbors Algorithm for Outliers in the Training Set Are So Close to the Test Element

Density Weighted K-Nearest Neighbors Algorithm for Outliers in the Training Set Are So Close to the Test Element

Automatic Identification of Traditional Colombian Music Genres Based on Audio Content Analysis and Machine Learning Techniques

A Review of various KNN Techniques

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