Nearest Neighbor Distributions for imbalanced classification

Kriminger, Evan; Prı́ncipe, José C.; Lakshminarayan, Choudur

doi:10.1109/ijcnn.2012.6252718

Cited by 24 publications

(14 citation statements)

References 5 publications

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“…It reduces the biasing towards majority class prediction in traditional KNN. A single-class algorithm called Class Conditional Nearest Neighbor Distribution (CCNND), which mitigates the effects of class imbalances through local geometric structure in the data was addressed by Evan Kriminger et al [29]. CCNND maintains high sensitivity to the minority class.…”

Section: ) Some Other Variations In Knnmentioning

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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Section: ) Some Other Variations In Knnmentioning

confidence: 99%

A Review of various KNN Techniques

Rani¹

2017

IJRASET

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“…Various modified nearest neighbor algorithms have been proposed in crisp and fuzzy manner with and without weights, some are discussed here. CCNND, a single class algorithm to minimize the classification cost is proposed by Kriminger et al [14], they applied local geometric structure in data for this purpose. This algorithm is applicable on multiclass data too.…”

Section: Related Workmentioning

confidence: 99%

Improved Fuzzy-Optimally Weighted Nearest Neighbor Strategy to Classify Imbalanced Data

Patel¹,

Thakur²

2017

IJIES

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Abstract:Learning from imbalanced data is one of the burning issues of the era. Traditional classification methods exhibit degradation in their performances while dealing with imbalanced data sets due to skewed distribution of data into classes. Among various suggested solutions, instance based weighted approaches secured the space in such cases. In this paper, we are proposing a new fuzzy weighted nearest neighbor method that optimally handle the imbalance issue of data. Use of optimal weights improve the performance of fuzzy nearest neighbor algorithm for default balanced distribution of data, for the classification of imbalanced data concept of adaptive K is merged with it that apply large K, number of nearest neighbors for large class and small K for small class. We deploy this combination to classify imbalanced data with better accuracy for different evaluation measures. Experimental results affirm that our proposed method perform well than the traditional fuzzy nearest neighbor classification for these type of data sets.

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“…al. [13], they applied local geometric structure in data. This approach is applicable on multi class imbalance as well as allows classification for different degrees of imbalance.…”

Section: Related Workmentioning

confidence: 99%

Classification of Imbalanced Data Using a Modified Fuzzy-Neighbor Weighted Approach

Patel¹,

Thakur²

2017

IJIES

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Classification of imbalanced datasets is one of the widely explored challenges of the decade. The imbalance occurs in many real world datasets due to uneven distribution of data into classes, i.e. one class has more instances while others have a few that results in the biased performances of traditional classifiers towards the majority class with large number of instances and ignorance of other classes with less data. Many solutions have been proposed to deal with this issue in various crisp and fuzzy methods. This paper proposes a new hybrid fuzzy weighted nearest neighbor approach to find better overall classification performance for both minority and majority classes of imbalanced data. Benefits of neighbor weighted K nearest neighbor approach i.e. assignment of large weights to small classes and small weights to large classes are merged with fuzzy logic. Fuzzy classification helps in classifying objects more adequately as it determines that how much an object belongs to a class. Experimental results exhibit the improvements in classification of imbalanced data of different imbalance ratios in comparison with other methods.

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Nearest Neighbor Distributions for imbalanced classification

Cited by 24 publications

References 5 publications

A Review of various KNN Techniques

A Review of various KNN Techniques

Improved Fuzzy-Optimally Weighted Nearest Neighbor Strategy to Classify Imbalanced Data

Classification of Imbalanced Data Using a Modified Fuzzy-Neighbor Weighted Approach

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