A method of learning weighted similarity function to improve the performance of nearest neighbor

Jahromi, Mansoor Zolghadri; Parvinnia, Elham; John, Robert

doi:10.1016/j.ins.2009.04.012

Cited by 78 publications

(38 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, a new method WDk NN was introduced in [9] which discovers optimal weights for each instance in training phase which are taken into account during test phases. This method is demonstrated superior to other k NN algorithm including LPD [10], PW [11], A-NN [12] and WDNN [13].…”

Section: Introductionmentioning

confidence: 87%

Class Confidence Weighted kNN Algorithms for Imbalanced Data Sets

Liu

Chawla

2011

Lecture Notes in Computer Science

120

View full text Add to dashboard Cite

Abstract. In this paper, a novel k -nearest neighbors (k NN) weighting strategy is proposed for handling the problem of class imbalance. When dealing with highly imbalanced data, a salient drawback of existing k NN algorithms is that the class with more frequent samples tends to dominate the neighborhood of a test instance in spite of distance measurements, which leads to suboptimal classification performance on the minority class. To solve this problem, we propose CCW (class confidence weights) that uses the probability of attribute values given class labels to weight prototypes in k NN. The main advantage of CCW is that it is able to correct the inherent bias to majority class in existing k NN algorithms on any distance measurement. Theoretical analysis and comprehensive experiments confirm our claims.

show abstract

Section: Introductionmentioning

confidence: 87%

Class Confidence Weighted kNN Algorithms for Imbalanced Data Sets

Liu

Chawla

2011

Lecture Notes in Computer Science

120

View full text Add to dashboard Cite

show abstract

“…Given a class of things, we always can find factor space to describe them. On the other hand, once a factor set describing a thing is selected, you can use it to describe a class of the thing, and in some sense the description is the only [24][25][26][27]. …”

Section: Formal Description Of Factor Representationmentioning

confidence: 99%

A novel knowledge representation model based on factor state space

Yang

Geng

Cao

2016

Optik

View full text Add to dashboard Cite

“…The kNN classifier classifies each unlabeled sample by the major label of its k nearest neighbors in the training dataset. Weighted Distance Nearest Neighbor (WDNN) [18] is a recent work on prototype reduction based on retaining the informative instances and learning their weights to improve the classification rate on training data. The WDNN algorithm is well formulated, and shows encouraging performance; however, it can only work with k=1 in practice.…”

Section: Related Workmentioning

confidence: 99%

Nearest Neighbor Classification with Locally Weighted Distance for Imbalanced Data

Hajizadeh¹,

Taheri²,

Jahromi³

2014

IJCCE

Self Cite

View full text Add to dashboard Cite

Abstract-The datasets used in many real applications are highly imbalanced which makes classification problem hard. Classifying the minor class instances is difficult due to bias of the classifier output to the major classes. Nearest neighbor is one of the most popular and simplest classifiers with good performance on many datasets. However, correctly classifying the minor class is commonly sacrificed to achieve a better performance on others. This paper is aimed to improve the performance of nearest neighbor in imbalanced domains, without disrupting the real data distribution. Prototype-weighting is proposed, here, to locally adapting the distances to increase the chance of prototypes from minor class to be the nearest neighbor of a query instance. The objective function is, here, G-mean and optimization process is performed using gradient ascent method. Comparing the experimental results, our proposed method significantly outperformed similar works on 24 standard data sets.Index Terms-Gradient ascent, imbalanced data, nearest neighbor, weighted distance.

show abstract

A method of learning weighted similarity function to improve the performance of nearest neighbor

Cited by 78 publications

References 18 publications

Class Confidence Weighted kNN Algorithms for Imbalanced Data Sets

Class Confidence Weighted kNN Algorithms for Imbalanced Data Sets

A novel knowledge representation model based on factor state space

Nearest Neighbor Classification with Locally Weighted Distance for Imbalanced Data

Contact Info

Product

Resources

About