New rank methods for reducing the size of the training set using the nearest neighbor rule

Rico-Juan, Juan Ramón; Iñesta, José M.

doi:10.1016/j.patrec.2011.07.019

Cited by 27 publications

(24 citation statements)

References 12 publications

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“…-Farther Neighbor (FN) [31]: gives a probability mass value to each prototype following a voting heuristic based on neighborhood. Prototypes are selected according to a parameter (fixed to 0.3 in our case) that indicates the probability mass desired for each class in the reduced set.…”

Section: Prototype Selection (Ps) Algorithmsmentioning

confidence: 99%

Prototype generation on structural data using dissimilarity space representation

Calvo-Zaragoza

Valero-Mas

Rico-Juan

2016

Neural Comput & Applic

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Data Reduction techniques play a key role in instance-based classification to lower the amount of data to be processed. Among the different existing approaches, Prototype Selection (PS) and Prototype Generation (PG) are the most representative ones. These two families differ in the way the reduced set is obtained from the initial one: while the former aims at selecting the most representative elements from the set, the latter creates new data out of it. Although PG is considered to delimit more efficiently decision boundaries, the operations required are not so well defined in scenarios involving structural data such as strings, trees or graphs. This work studies the possibility of using Dissimilarity Space (DS) methods as an intermediate process for mapping the initial structural representation to a statistical one, thereby allowing the use of PG methods. A comparative experiment over string data is carried out in which our proposal is faced to PS methods on the original space. Results show that the proposed strategy is able to achieve significantly similar results to PS in the initial space, thus standing as a clear alternative to the classic approach, with some additional advantages derived from the DS representation.

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Section: Prototype Selection (Ps) Algorithmsmentioning

confidence: 99%

Prototype generation on structural data using dissimilarity space representation

Calvo-Zaragoza

Valero-Mas

Rico-Juan

2016

Neural Comput & Applic

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“…Both strategies are based on the idea that a prototype can give one vote to another one, and the question is to decide which prototype it is given to. Although these strategies were previously published [22], we shall revisit below their main ideas for a better readability of the current paper.…”

Section: Rank Methods For Prototype Selectionmentioning

confidence: 99%

“…We will denote these strategies by n-FN and n-NE. For example, configuration n = 2 will use the second nearest enemy to voting prototype a, which has proved to be useful in practice [22].…”

Section: Nearest To Enemy Votingmentioning

confidence: 99%

An experimental study on rank methods for prototype selection

et al. 2016

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Prototype Selection is one of the most popular approaches for addressing the low efficiency issue typically found in the well-known k-Nearest Neighbour classification rule. These techniques select a representative subset from an original collection of prototypes with the premise of maintaining the same classification accuracy. Most recently, rank methods have been proposed as an alternative to develop new selection strategies. Following a certain heuristic, these methods sort the elements of the initial collection according to their relevance and then select the best possible subset by means of a parameter representing the amount of data to maintain. Due to the relative novelty of these methods, their performance and competitiveness against other strategies is still unclear. This work performs an exhaustive experimental study of such methods for prototype selection. A representative collection of both classic and sophisticated algorithms are compared to the aforementioned techniques in a number of datasets, including different levels of induced noise. Results report the remarkable competitiveness of these rank methods as well as their excellent trade-off between prototype reduction and achieved accuracy.

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“…It computes a fast, orderindependent condensing strategy based on seeking the centroids of each label. -The Nearest to Enemy (NE) rule [22] gives a probability mass value to each prototype following a voting heuristic based on neighboring criteria. Prototypes are selected according to a parameter (fixed to 0.3 in our case) that indicates the probability mass desired for each class in the reduced set.…”

Section: Prototype Selectionmentioning

confidence: 99%

Selecting promising classes from generated data for an efficient multi-class nearest neighbor classification

2016

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The Nearest Neighbor rule is one of the most considered algorithms for supervised learning because of its simplicity and fair performance in most cases. However, this technique has a number of disadvantages, being the low computational efficiency the most prominent one. This paper presents a strategy to overcome this obstacle in multi-class classification tasks. This strategy proposes the use of Prototype Reduction algorithms that are capable of generating a new training set from the original one to try to gather the same information with fewer samples. Over this reduced set, it is estimated which classes are the closest ones to the input sample. These classes are referred to as promising classes. Eventually, classification is performed using the original training set using the Nearest Neighbor rule but restricted to the promising classes. Our experiments with several datasets and significance tests show that a similar classification accuracy can be obtained compared to using the original training set, with a significantly higher efficiency.

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New rank methods for reducing the size of the training set using the nearest neighbor rule

Cited by 27 publications

References 12 publications

Prototype generation on structural data using dissimilarity space representation

Prototype generation on structural data using dissimilarity space representation

An experimental study on rank methods for prototype selection

Selecting promising classes from generated data for an efficient multi-class nearest neighbor classification

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