Algorithms for Identification Key Generation and Optimization with Application to Yeast Identification

Reynolds, Alan; Dicks, Jo; Roberts, Ian N.; Wesselink, Jan-Jap; Iglesia, Beatriz de la; Boekhout, Teun; Rayward‐Smith, V. J.

doi:10.1007/3-540-36605-9_11

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…A common approach to identify species relies on dichotomous keys (Pankhurst, 1975;Payne & Preece, 1980). Extensive computer applications and theory have been developed to generate keys (Osbourne, 1963;Dallwitz, 1974;Payne & Preece, 1980;Pankhurst, 1986;Payne & Thompson, 1989;Reynolds & al., 2003;Delta and Intkey: Dallwitz & al. 2000; Navikey: http://www.…”

Section: Integration With Multiple-entry Dichotomous Keysmentioning

confidence: 99%

“…The quality of the trait characterization provided by an ANN can also be incorporated into multiple-entry keys. Much work has focused on the ability to include information about quality of traits, polymorphic traits, error in trait characterization, and prior probabilities of species labeling (Dallwitz, 1974;Payne & Preece, 1980;Pankhurst, 1986;Payne & Thompson, 1989;Reynolds & al., 2003), but these considerations were not included in the derivation of equation [7], which relates accuracy to number of characters used. Errors in trait assessment and identification using keys can be quite large in practical situations (Fermanian & al., 1989), and when character states are assessed incorrectly in the field or misrepresented in the database, the identification will fail no matter how many characters are considered.…”

Section: Integration With Multiple-entry Dichotomous Keysmentioning

confidence: 99%

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Shape analysis for the automated identification of plants from images of leaves

Hearn

2009

TAXON

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Species identification is a necessary component of most studies of biological diversity, and computational approaches are beginning to automate it. In particular, leaves of plants provide taxon‐specific information that has successfully been applied to plant identification. Prior studies have not investigated the number of leaves or the resolution of the digitized leaf image required to represent a species’ shape. Moreover, the relationship between accuracy and the size of the leaf shape database, and methods to integrate automated approaches with more traditional dichotomous keys have yet to be explored. Here, I use a database of 2,420 leaves from 151 species to address these issues. Using distance metrics derived from Fourier and Procrustes analyses, it is found that a minimum of 10 leaves of each species, 100 margin points, and 10 Fourier harmonics are required to accurately represent leaf shape of a species. These results are used to assess the success of species identification from images of leaves: 72% for all 151 species. The tight relationship between database size and accuracy is then used in conjunction with results from probability theory to predict accuracy of species identification when dichotomous multiple‐entry keys and combined Fourier and Procrustes analysis are used together. Combining these two approaches to identification can greatly improve identification accuracy. Open‐source software is available to implement the automated distance‐based approach.

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Section: Integration With Multiple-entry Dichotomous Keysmentioning

confidence: 99%

Section: Integration With Multiple-entry Dichotomous Keysmentioning

confidence: 99%

Shape analysis for the automated identification of plants from images of leaves

Hearn

2009

TAXON

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“…These programs have varying degrees of mathematical sophistication, accuracy, ease of use, capacity, and limitations on character types. Examples of such programs include Pankey (Pankhurst, 1970(Pankhurst, , 1986, DELTA (Dallwitz et al, 1993 onwards;Dallwitz & Paine, 2005;Dallwitz 2010), Xkey (Calvo-Flores et al, 2006), SIKey (Zhang et al, 2008), and the program described by Reynolds et al (2003).…”

Section: Introductionmentioning

confidence: 99%

Computer-Generated Keys and Descriptions of the Flora of Saudi Arabia: A Pilot Study on Some Grasses from Jazan

2021

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T HIS IS THE FIRST use of computer programs to construct keys and descriptions of plants in the flora of Saudi Arabia or any other country in the Arabian Peninsula. The program suit DELTA was used to generate a conventional key to 55 species of Poaceae collected from Jazan region, Saudi Arabia, on the basis of a dataset comprising 50 easily observable morphological characters. Together with the key, the outcome of the computer run includes descriptions for individual species in natural language based on the entirety of the 50 characters recorded for every species to be used in the confirmation of its identity. The computer-generated key avoids the numerous inconsistencies encountered frequently in its traditional counterparts, and can easily be regenerated to accommodate additional taxa and/or characters. This study was undertaken as a model for other studies aiming at improving floristic studies in Saudi Arabia and the procedure adopted here is equally applicable to all other groups of wild and cultivated plants in the the country.

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“…Computer programs and algorithms for identification and diagnosis applications have been developed for nearly four decades (e.g., Pankhurst [1970], Reynolds et al [2003], and Wijtzes et al [1997]). Murthy [1998] and Moret [1982] present excellent surveys on the use of decision trees in such diverse fields as machine learning, pattern recognition, taxonomy, switching theory, and boolean logic.…”

Section: Introductionmentioning

confidence: 99%

Decision trees for entity identification

Chakaravarthy

Pandit

Roy

et al. 2011

ACM Trans. Algorithms

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We consider the problem of constructing decision trees for entity identification from a given relational table. The input is a table containing information about a set of entities over a fixed set of attributes and a probability distribution over the set of entities that specifies the likelihood of the occurrence of each entity. The goal is to construct a decision tree that identifies each entity unambiguously by testing the attribute values such that the average number of tests is minimized. This classical problem finds such diverse applications as efficient fault detection, species identification in biology, and efficient diagnosis in the field of medicine. Prior work mainly deals with the special case where the input table is binary and the probability distribution over the set of entities is uniform. We study the general problem involving arbitrary input tables and arbitrary probability distributions over the set of entities. We consider a natural greedy algorithm and prove an approximation guarantee of O(r K · log N), where N is the number of entities and K is the maximum number of distinct values of an attribute. The value r K is a suitably defined Ramsey number, which is at most log K. We show that it is NP-hard to approximate the problem within a factor of (log N), even for binary tables (i.e., K = 2). Thus, for the case of binary tables, our approximation algorithm is optimal up to constant factors (since r 2 = 2). In addition, our analysis indicates a possible way of resolving a Ramsey-theoretic conjecture by Erdös.

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Algorithms for Identification Key Generation and Optimization with Application to Yeast Identification

Cited by 6 publications

References 6 publications

Shape analysis for the automated identification of plants from images of leaves

Shape analysis for the automated identification of plants from images of leaves

Computer-Generated Keys and Descriptions of the Flora of Saudi Arabia: A Pilot Study on Some Grasses from Jazan

Decision trees for entity identification

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