A new iterative algorithm for computing a quality approximate median of strings based on edit operations

Abreu, José Ignacio; Rico-Juan, Juan Ramón

doi:10.1016/j.patrec.2013.09.014

Cited by 17 publications

(37 citation statements)

References 17 publications

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“…Serve as an example the median operation: its computation is easy for n-dimensional points whereas it becomes NP-complete when points are strings [22]. Some examples of works addressing related issues include the work of Abreu and Rico-Juan [1], in which the median of a string set is approximated using edit operations, or Ferrer and Bunke [16], in which an iterative algorithm for the computation of the median operation on graphs is exposed. Nevertheless, all of them take advantage of the knowledge of the specific structural data to create these new prototypes.…”

Section: Prototype Generation Over Structural Data Using Dissimilaritmentioning

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 Generation Over Structural Data Using Dissimilaritmentioning

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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“…This inconsistency is well known [12], and there have been a number of studies addressing this issue [8,21], with recently a specific analysis of the relationship between 0/1 loss functions and other discrete loss functions [22]. Other approaches include the introduction of heuristics to approximate the median string [13,15,1].…”

Section: Resultsmentioning

confidence: 99%

Computing the Expected Edit Distance from a String to a PFA

Calvo-Zaragoza

Higuera

Oncina

2016

Implementation and Application of Automata

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Abstract. In a number of fields one is to compare a witness string with a distribution. One possibility is to compute the probability of the string for that distribution. Another, giving a more global view, is to compute the expected edit distance from a string randomly drawn to the witness string. This number is often used to measure the performance of a prediction, the goal then being to return the median string, or the string with smallest expected distance. To be able to measure this, computing the distance between a hypothesis and that distribution is necessary. This paper proposes two solutions for computing this value, when the distribution is defined with a probabilistic finite state automaton. The first is exact but has a cost which can be exponential in the length of the input string, whereas the second is a Fpras.

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“…Instead of applying operations one by one, other author [3] propose to apply multiple perturbations at once. Results in [1] suggest that this approach has a fastest convergence but the quality of the approximated median is worst.…”

Section: Related Workmentioning

confidence: 99%

“…In the experimental evaluation, we show that this bound is rather pessimistic and our heuristic usually needs just a few operations per iteration. This is a key difference with the algorithm in [1], which uses more operations per iteration. For each operation explored during an iteration, the algorithm computes the distance of the new candidate R 1 to all the elements in S (lines [16][17][18][19], which takes time OpNˆdcq, where dc is the time to compute the edit distance and depends on the specific measure used.…”

Section: Opmentioning

confidence: 99%

Assessing the best edit in perturbation-based iterative refinement algorithms to compute the median string

Mirabal

Abreu

Seco

2019

Pattern Recognition Letters

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Strings are a natural representation of biological data such as DNA, RNA and protein sequences. The problem of finding a string that summarizes a set of sequences has direct application in relative compression algorithms for genome and proteome analysis, where reference sequences need to be chosen. Median strings have been used as representatives of a set of strings in different domains. However, several formulations of those problems are NP-Complete. Alternatively, heuristic approaches that iteratively refine an initial coarse solution by applying edit operations have been proposed. Recently, we investigated the selection of the optimal edit operations to speed up convergence without spoiling the quality of the approximated median string. We propose a novel algorithm that outperforms state of the art heuristic approximations to the median string in terms of convergence speed by estimating the effect of a perturbation in the minimization of the expressions that define the median strings. We present corpus of comparative experiments to validate these results.

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A new iterative algorithm for computing a quality approximate median of strings based on edit operations

Cited by 17 publications

References 17 publications

Prototype generation on structural data using dissimilarity space representation

Prototype generation on structural data using dissimilarity space representation

Computing the Expected Edit Distance from a String to a PFA

Assessing the best edit in perturbation-based iterative refinement algorithms to compute the median string

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