A new approach to differential diagnosis of diseases

Ortiz-Posadas, M. R.; Martínez-Trinidad, José Fco.; Ruiz-Shulcloper, José

doi:10.1016/0020-7101(95)01143-9

Cited by 22 publications

(7 citation statements)

References 13 publications

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“…The Logical Combinatorial Pattern Recognition (LCPR) has as main characteristic that allow us handle information of the objects (their descriptions) under study in terms of a set of features or attributes [5]. These features may be of any nature, that is to say, numeric or not numeric.…”

Section: Typical Testors By Classmentioning

confidence: 99%

Discovering Differences in Patients with Uveitis through Typical Testors by Class

Trinidad¹,

Velasco-Sánchez

Contreras-Aravelo

2000

Principles of Data Mining and Knowledge Discovery

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Section: Typical Testors By Classmentioning

confidence: 99%

Discovering Differences in Patients with Uveitis through Typical Testors by Class

Trinidad¹,

Velasco-Sánchez

Contreras-Aravelo

2000

Principles of Data Mining and Knowledge Discovery

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“…So, an irreducible testor has the same capability as the entire feature set to discern between objects belonging to different classes. This is the reason why irreducible testors have been used for solving several problems related to supervised pattern recognition (see for example [11], [20], [25], [29], [47]).…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Computing Minimum-Length Irreducible Testors

et al. 2020

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In pattern recognition, the elimination of unnecessary and/or redundant attributes is known as feature selection. Irreducible testors have been used to perform this task. An objective of the Minimum Description Length Principle (MDL) applied to feature selection in pattern recognition and data mining is to select the minimum number of attributes in a data set. Consequently, the MDL principle leads us to consider the subset of irreducible testors of minimum length. Some algorithms that find the whole set of irreducible testors have been reported in the literature. However, none of these algorithms was designed to generate only minimum-length irreducible testors. In this paper, we propose the first algorithm specifically designed to calculate all minimum-length irreducible testors from a training sample. The paper presents some experimental results obtained using synthetic and real data in which the performance of the proposed algorithm is contrasted with other state-of-the-art algorithms that were adapted to generate only irreducible testers of minimum length. INDEX TERMS Feature selection, MDL principle, minimum-length irreducible testors, testor.

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“…Typical testors play an important role in solving some feature selection [4] [14], diagnosis of diseases [8], text categorization [10], document summarization [9] and document clustering [6]. The concept of typical testor has been extended and generalized in several ways [5].…”

Section: Introductionmentioning

confidence: 99%

A Theoretical and Practical Framework for Assessing the Computational Behavior of Typical Testor-Finding Algorithms

Alba-Cabrera

Ibarra-Fiallo

Godoy-Calderón

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Although the general relevance of Testor Theory as the theoretical ground for useful feature selection procedures is well known, there are no practical means, nor any standard methodologies, for assessing the behavior of a testor-finding algorithm when faced with specific circumstances. In this work, we present a practical framework, with proven theoretical foundation, for assessing the behavior of both deterministic and meta-heuristic testor-finding algorithms when faced with specific phenomena.

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A new approach to differential diagnosis of diseases

Cited by 22 publications

References 13 publications

Discovering Differences in Patients with Uveitis through Typical Testors by Class

Discovering Differences in Patients with Uveitis through Typical Testors by Class

An Algorithm for Computing Minimum-Length Irreducible Testors

A Theoretical and Practical Framework for Assessing the Computational Behavior of Typical Testor-Finding Algorithms

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