Median strings for k-nearest neighbour classification

Martínez-Hinarejos, Carlos D.; Juan, Alfons; Casacuberta, Francisco

doi:10.1016/s0167-8655(02)00209-x

Cited by 28 publications

(12 citation statements)

References 10 publications

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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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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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“…Kohonen, in 1985 [4], proposed an approximation algorithm to find the generalized median based on a process of perturbation of the set median in order to modify it in the direction of the generalized median string. This results was improved in 2000 by Martínez-Hinarejos et al [5] [6]. Moreover, their experiments showed that using the generalized median instead of the set median improved their results.…”

Section: Introductionmentioning

confidence: 89%

A Stochastic Approach to Median String Computation

Olivares-Rodríguez

Oncina

2008

Lecture Notes in Computer Science

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Abstract. Due to its robustness to outliers, many Pattern Recognition algorithms use the median as a representative of a set of points. A special case arises in Syntactical Pattern Recognition when the points (prototypes) are represented by strings. However, when the edit distance is used, finding the median becomes a NP-Hard problem. Then, either the search is restricted to strings in the data (set-median) or some heuristic approach is applied. In this work we use the (conditional) stochastic edit distance instead of the plain edit distance. It is not yet known if in this case the problem is also NP-Hard so an approximation algorithm is described. The algorithm is based on the extension of the string structure to multistrings (strings of stochastic vectors where each element represents the probability of each symbol) to allow the use of the Expectation Maximization technique. We carry out some experiments over a chromosomes corpus to check the efficiency of the algorithm.

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“…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. By providing a better ranking, we save on the number of operations explored per iteration, and thus, on the number of times this distance is computed, which is expensive.…”

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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Median strings for k-nearest neighbour classification

Cited by 28 publications

References 10 publications

Computing the Expected Edit Distance from a String to a PFA

Computing the Expected Edit Distance from a String to a PFA

A Stochastic Approach to Median String Computation

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

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