An affinity-based new local distance function and similarity measure for kNN algorithm

Bhattacharya, Gautam; Ghosh, Koushik; Chowdhury, Ananda S.

doi:10.1016/j.patrec.2011.10.021

Cited by 56 publications

(24 citation statements)

References 25 publications

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“…However, Bhattacharyya [24] proposes a bound to the optimal k, that is k < √ m. In binary classification, one can restrain the range of k by using only odd values in order to avoid ties in equation (2.1). The class posterior distribution, p(ω j |x), is an alternative to the optimum Bayesian classification approach, which requires the complete knowledge of data generation underlying mechanisms.…”

Section: K-nearest Neighbors (Knn) Algorithmmentioning

confidence: 99%

Wrappers Feature Selection in Alzheimer's Biomarkers Using kNN and SMOTE Oversampling

Rodrigues¹,

Manica²,

Zimmer³

et al. 2017

Tend. Mat. Apl. Comput.

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ABSTRACT. Biomarkers are characteristics that are objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacological responses to a therapeutic intervention. The combination of different biomarker modalities often allows an accurate diagnosis classification. In Alzheimer's disease (AD), biomarkers are indispensable to identify cognitively normal individuals destined to develop dementia symptoms. However, using the combination of canonical AD biomarkers, studies have repeatedly shown poor classification rates to differentiate between AD, mild cognitive impairment and control individuals. Furthermore, the design of classifiers to access multiple biomarker combinations includes issues such as imbalance classes and missing data. Due to the number of biomarkers combinations wrappers are used to avoid multiple comparisons. Here, we compare the ability of three wrappers feature selection methods to obtain biomarker combinations which maximize classification rates. Also, as the criterion to the wrappers feature selection we use the k-nearest neighbor classifier with balance aids, random undersampling and SMOTE oversampling. Overall, our analyses showed how biomarkers combinations affect the classifier precision and how imbalance strategy improve it. We show that non-defining and non-cognitive biomarkers have less precision than cognitive measures when classifying AD. Our approach surpasses in average the support vector machine and the weighted k-nearest neighbor classifiers and reaches 94.34 ± 3.91% of precision reproducing class definitions.

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Section: K-nearest Neighbors (Knn) Algorithmmentioning

confidence: 99%

Wrappers Feature Selection in Alzheimer's Biomarkers Using kNN and SMOTE Oversampling

Rodrigues¹,

Manica²,

Zimmer³

et al. 2017

Tend. Mat. Apl. Comput.

View full text Add to dashboard Cite

show abstract

“…As in [4], the time complexities of Algorithm 1 can be analyzed as follows: as in the case of SemiBoost, almost all of the processing CPUtime of the algorithm is consumed in computing the confidence levels and training the classifier. More specifically, the time complexity for each step can be analyzed as follows …”

Section: End Algorithmmentioning

confidence: 99%

Modified criterion to select useful unlabeled data for improving semi-supervised support vector machines

Kim

2015

Pattern Recognition Letters

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“…The classification error rate of nearest neighbor is not more than twice the Bayes [10] where the number of training instances is sufficiently large. Even in nearest neighbor classifier that has no training phase, without any priority knowledge of the query instance, it is more likely that the nearest neighbor is a prototype from the major class.…”

Section: Introductionmentioning

confidence: 99%

Nearest Neighbor Classification with Locally Weighted Distance for Imbalanced Data

Hajizadeh¹,

Taheri²,

Jahromi³

2014

IJCCE

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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.

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An affinity-based new local distance function and similarity measure for kNN algorithm

Cited by 56 publications

References 25 publications

Wrappers Feature Selection in Alzheimer's Biomarkers Using kNN and SMOTE Oversampling

Wrappers Feature Selection in Alzheimer's Biomarkers Using kNN and SMOTE Oversampling

Modified criterion to select useful unlabeled data for improving semi-supervised support vector machines

Nearest Neighbor Classification with Locally Weighted Distance for Imbalanced Data

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