Optimal unions of hidden classes

Hrebik, Radek; Kukal, Jaromír; Jablonský, Josef

doi:10.1007/s10100-017-0496-5

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…The system of hidden classes arises from uncertainty of class membership, imperfectness of classification or any context out approach. We will apply a deterministic approach which is based on the relationship between hidden groups and the output classes (Hrebik et al 2019) as follows. The aim is to optimize this relationship as the best mapping from the hidden to the output classes.…”

Section: Union Of Hidden Classesmentioning

confidence: 99%

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Concept of hidden classes in pattern classification

Hrebik

Kukal

2023

Artif Intell Rev

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Our paper presents a novel approach to pattern classification. The general disadvantage of a traditional classifier is in too different behaviour and optimal parameter settings during training on a given pattern set and the following cross-validation. We describe the term critical sensitivity, which means the lowest reached sensitivity for an individual class. This approach ensures a uniform classification quality for individual class classification. Therefore, it prevents outlier classes with terrible results. We focus on the evaluation of critical sensitivity, as a quality criterion. Our proposed classifier eliminates this disadvantage in many cases. Our aim is to present that easily formed hidden classes can significantly contribute to improving the quality of a classifier. Therefore, we decided to propose classifier will have a relatively simple structure. The proposed classifier structure consists of three layers. The first is linear, used for dimensionality reduction. The second layer serves for clustering and forms hidden classes. The third one is the output layer for optimal cluster unioning. For verification of the proposed system results, we use standard datasets. Cross-validation performed on standard datasets showed that our critical sensitivity-based classifier provides comparable sensitivity to reference classifiers.

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Section: Union Of Hidden Classesmentioning

confidence: 99%

“…We propose using simple clustering methods resulting in more clusters than the final classes, which will not be many or few. After forming such clusters, we propose a union of them with the optimal union method (Hrebik et al 2019). Such unioning enables the formation of a classifier with the highest possible critical sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Concept of hidden classes in pattern classification

Hrebik

Kukal

2023

Artif Intell Rev

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“…For example the concentration of gold is very low and therefore no piece of stone would be selected for future gold extraction. This paradox can be easily solved by using cluster sensitivity and critical sensitivity which help to construct more sophisticated strategies not only for ore mining but mainly for general classification of patterns [6]. A general classification task distributes m patterns into N classes, and our method is based on preprocessing which places them into H hidden classes.…”

Section: Motivationmentioning

confidence: 99%

Context Out Classifier

Hrebik¹,

Kukal²

2018

mendel

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Novel context out learning approach is discussed as possibility of using simple classifiers which is background of hidden class system. There are two ways how to perform final classification. Having a lot of hidden classes we can build their unions using binary optimization task. Resulting system has the best possible sensitivity over all output classes. Another way is to perform second level linear classification as referential approach. The presented techniques are demonstrated on traditional iris flower task.

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