SV-kNNC: An Algorithm for Improving the Efficiency of k-Nearest Neighbor

Srisawat, Anantaporn; Phienthrakul, Tanasanee; Kijsirikul, Boonserm

doi:10.1007/978-3-540-36668-3_117

Cited by 14 publications

(8 citation statements)

References 5 publications

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“…A wrapper method based on SVM is proposed in Yuangui et al (2005) which works doing a double selection; the first one applies SVM to obtain V s and the second one applies DROP2 over V s . Another method related to instance selection based on SVM is SV-kNNC (Support Vector k-Nearest Neighbor Clustering) (Srisawat et al 2006) which after applying SVM over T uses the k-means algorithm for clustering V s and retains only such instances belonging to homogeneous clusters (clusters where all instances belong to the same class); when the cluster is non homogeneous, only such instances from the majority class in the cluster are preserved.…”

Section: Wrapper Methodsmentioning

confidence: 99%

A review of instance selection methods

et al. 2010

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In supervised learning, a training set providing previously known information is used to classify new instances. Commonly, several instances are stored in the training set but some of them are not useful for classifying therefore it is possible to get acceptable classification rates ignoring non useful cases; this process is known as instance selection. Through instance selection the training set is reduced which allows reducing runtimes in the classification and/or training stages of classifiers. This work is focused on presenting a survey of the main instance selection methods reported in the literature.

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Section: Wrapper Methodsmentioning

confidence: 99%

A review of instance selection methods

et al. 2010

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“…Thereby, numbers of intervals and length of intervals would be produced based on the dataset. An efficient algorithm [15] has been applied to carry out this step. The SV-kNNC algorithm has less time classification than KNN.…”

Section: Phase Ii: Merging Intervalsmentioning

confidence: 99%

Dynamic data discretization technique based on frequency and K-Nearest Neighbour algorithm

Ahmed

Bakar

Hamdan

2009

2009 2nd Conference on Data Mining and Optimization

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In this paper we propose a new approach to the dynamic data discretization technique. The technique is called Frequency Dynamic Interval Class (FDIC). FDIC consists of two important phases: The dynamic intervals class phase and the interval merging phase. The first phase uses a simple statistical frequency measure to obtain the initial intervals while in the second phase a KNearest Neighbour is used to calculate the merging factor for the unknown intervals. The experimental results showed that FDIC generates more intervals in an attribute, and less number rules with comparable accuracies within three tested datasets. It indicates that FDIC managed to reduce the loss of knowledge in several other techniques that generated the very least number of intervals.

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“…To this end, the literature provides a variety of approaches. There are instance selection methods deliberately designed for specific classifiers including K−nearest neighbors [ e.g., [11][12][13][14][15] and support vector machine [ e.g., [16][17][18]. Also, there are studies on more general methods mostly constructed on a prescribed distance metrics and applications [ e.g., [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Optimal training and test sets design for machine learning

Genç¹,

Tunç²

2019

Turk J Elec Eng & Comp Sci

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In this paper, we describe histogram matching, a metric for measuring the distance of two datasets with exactly the same features, and embed it into a mixed integer programming formulation to partition a dataset into fixed size training and test subsets. The partition is done such that the pairwise distances between the dataset and the subsets are minimized with respect to histogram matching. We then conduct a numerical study using a well-known machine learning dataset. We demonstrate that the training set constructed with our approach provides feature distributions almost the same as the whole dataset, whereas training sets constructed via random sampling end up with significant differences. We also show that our method introduces neither positive nor negative bias in prediction accuracy of a decision tree—used as a representative example of a machine learning method.

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SV-kNNC: An Algorithm for Improving the Efficiency of k-Nearest Neighbor

Cited by 14 publications

References 5 publications

A review of instance selection methods

A review of instance selection methods

Dynamic data discretization technique based on frequency and K-Nearest Neighbour algorithm

Optimal training and test sets design for machine learning

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