Nearest Neighbour Distance Matrix Classification

Sainin, Mohd Shamrie; Alfred, Rayner

doi:10.1007/978-3-642-17316-5_11

Cited by 2 publications

(4 citation statements)

References 8 publications

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“…the proposed GA wrapper based feature selection (GA+NNDM) using random initial population. The detailed results of the experiments on NNDM classification using Weka, 1SNNDM, kSNNDM and other algorithms can be found from the previous work in [6]. The results suggest that the application of the GA based wrapper feature selection can significantly improve the classification accuracy rate of the proposed NNDM.…”

Section: Experimental Design and Resultsmentioning

confidence: 85%

“…It consist of two competing term in which the first term pulls target neighbours closer, and second term pushes different labelled example away from the target instance. The basic implementation of NNDM has been discussed in [6].…”

Section: A the Nndm Algorithmmentioning

confidence: 99%

“…One of the methods is the application of nearest neighbour classifier as described in [13,23]. In the recent novel nearest neighbour classifier called as Nearest Neighbour Distance Matrix (NNDM) described in [6], the classifier has a promising performance and thus it is used as the classifier for the feature selection process described in this paper. In other word, inspired by the usages of GA in feature selection and NNDM algorithm above, a genetic based wrapper for feature selection is proposed.…”

Section: Introductionmentioning

confidence: 99%

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A genetic based wrapper feature selection approach using Nearest Neighbour Distance Matrix

Sainin

Alfred

2011

2011 3rd Conference on Data Mining and Optimization (DMO)

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Feature selection for data mining optimization receives quite a high demand especially on high-dimensional feature vectors of a data. Feature selection is a method used to select the best feature (or combination of features) for the data in order to achieve similar or better classification rate. Currently, there are three types of feature selection methods: filter, wrapper and embedded. This paper describes a genetic based wrapper approach that optimizes feature selection process embedded in a classification technique called a supervised Nearest Neighbour Distance Matrix (NNDM). This method is implemented and tested on several datasets obtained from the UCI Machine Learning Repository and other datasets. The results demonstrate a significant impact on the predictive accuracy for feature selection combined with the supervised NNDM in classifying new instances. Therefore it can be used in other applications that require feature dimension reduction such as image and bioinformatics classifications.

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Section: Experimental Design and Resultsmentioning

confidence: 85%

Section: A the Nndm Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A genetic based wrapper feature selection approach using Nearest Neighbour Distance Matrix

Sainin

Alfred

2011

2011 3rd Conference on Data Mining and Optimization (DMO)

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“…In order to create the benchmark pool, the publicly available dataset repositories were examined such as the UCI [35], KEEL [36], UCR Time Series [37], NIPS Feature Selection Challenge [38] and previously used dataset in multiclass imbalance publication (IEEE, ACM, Springer, Science Direct, etc). There are 16 selected datasets were chosen for multiclass imbalance data in 5 different domains, example size vary from 100 to 50,000, feature size change from less than 10 to 100 and imbalance ratio range from 1:2 to 1:4559.…”

Section: Experimental Design and Resultsmentioning

confidence: 99%

A Direct Ensemble Classifier for Imbalanced Multiclass Learning

Sainin

Alfred

2012

2012 4th Conference on Data Mining and Optimization (DMO)

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Researchers have shown that although traditional direct classifier algorithm can be easily applied to multiclass classification, the performance of a single classifier is decreased with the existence of imbalance data in multiclass classification tasks. Thus, ensemble of classifiers has emerged as one of the hot topics in multiclass classification tasks for imbalance problem for data mining and machine learning domain. Ensemble learning is an effective technique that has increasingly been adopted to combine multiple learning algorithms to improve overall prediction accuraciesand may outperform any single sophisticated classifiers. In this paper, an ensemble learner called a Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML) that combines simple nearest neighbour and Naive Bayes algorithms is proposed. A combiner method called OR-tree is used to combine the decisions obtained from the ensemble classifiers. The DECIML framework has been tested with several benchmark dataset and shows promising results.

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Nearest Neighbour Distance Matrix Classification

Cited by 2 publications

References 8 publications

A genetic based wrapper feature selection approach using Nearest Neighbour Distance Matrix

A genetic based wrapper feature selection approach using Nearest Neighbour Distance Matrix

A Direct Ensemble Classifier for Imbalanced Multiclass Learning

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