Data Reduction in the String Space for Efficient kNN Classification Through Space Partitioning

Valero-Mas, Jose J.; Castellanos, Francisco J.

doi:10.3390/app10103356

Cited by 10 publications

(6 citation statements)

References 26 publications

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“…Not only was the RHC algorithm was found to be much faster than RSP3, but it also was one of the fastest approaches that took part in this experimental study [10]. A modified version of the RHC algorithm has recently been applied on string data spaces [11,12].…”

Section: Related Workmentioning

confidence: 96%

Fast Training Set Size Reduction Using Simple Space Partitioning Algorithms

et al. 2022

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The Reduction by Space Partitioning (RSP3) algorithm is a well-known data reduction technique. It summarizes the training data and generates representative prototypes. Its goal is to reduce the computational cost of an instance-based classifier without penalty in accuracy. The algorithm keeps on dividing the initial training data into subsets until all of them become homogeneous, i.e., they contain instances of the same class. To divide a non-homogeneous subset, the algorithm computes its two furthest instances and assigns all instances to their closest furthest instance. This is a very expensive computational task, since all distances among the instances of a non-homogeneous subset must be calculated. Moreover, noise in the training data leads to a large number of small homogeneous subsets, many of which have only one instance. These instances are probably noise, but the algorithm mistakenly generates prototypes for these subsets. This paper proposes simple and fast variations of RSP3 that avoid the computationally costly partitioning tasks and remove the noisy training instances. The experimental study conducted on sixteen datasets and the corresponding statistical tests show that the proposed variations of the algorithm are much faster and achieve higher reduction rates than the conventional RSP3 without negatively affecting the accuracy.

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Section: Related Workmentioning

confidence: 96%

Fast Training Set Size Reduction Using Simple Space Partitioning Algorithms

et al. 2022

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“…This RHC method was recently adapted to string-based representations of Valero-Mas and Castellanos (2020). In this adaptation, the prototype generation stage of the RHC algorithm required the computation of the median value of a set of strings.…”

Section: Background In Data Reductionmentioning

confidence: 99%

“…In this line, a recent work by Valero-Mas and Castellanos (2020) proposed the implementation of the wellknown PG method reduction through homogeneous clusters (RHC) (Ougiaroglou and Evangelidis 2016) for kNN-based classification, in which data is represented as strings. This approach is based on recursively dividing the initial corpus into homogeneous clusters in order to then replace each of them with a representative prototype generated as the median element of the cluster.…”

Section: Introductionmentioning

confidence: 99%

Prototype generation in the string space via approximate median for data reduction in nearest neighbor classification

2021

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The k-nearest neighbor (kNN) rule is one of the best-known distance-based classifiers, and is usually associated with high performance and versatility as it requires only the definition of a dissimilarity measure. Nevertheless, kNN is also coupled with low-efficiency levels since, for each new query, the algorithm must carry out an exhaustive search of the training data, and this drawback is much more relevant when considering complex structural representations, such as graphs, trees or strings, owing to the cost of the dissimilarity metrics. This issue has generally been tackled through the use of data reduction (DR) techniques, which reduce the size of the reference set, but the complexity of structural data has historically limited their application in the aforementioned scenarios. A DR algorithm denominated as reduction through homogeneous clusters (RHC) has recently been adapted to string representations but as obtaining the exact median value of a set of string data is known to be computationally difficult, its authors resorted to computing the set-median value. Under the premise that a more exact median value may be beneficial in this context, we, therefore, present a new adaptation of the RHC algorithm for string data, in which an approximate median computation is carried out. The results obtained show significant improvements when compared to those of the set-median version of the algorithm, in terms of both classification performance and reduction rates.

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“…While this process may be seen as a trivial task, performing such a comparison is a remarkably time-consuming process, especially if the data structure consid-30 ered is somewhat complex. Thus, given that distance-based classifiers require the computation of the dissimilarity between the input query and every single element of the training data, the efficiency is considerably low [9].…”

Section: Introductionmentioning

confidence: 99%