Classifying Predicates and Languages

1997 ◽  
Vol 08 (01) ◽  
pp. 15-41 ◽  
Author(s):  
Carl H. Smith ◽  
Rolf Wiehagen ◽  
Thomas Zeugmann
Keyword(s):  
Learning Theory ◽  
Classification Problems ◽  
Attention In Learning ◽  
Recursive Functions ◽  
Standard Classification ◽  
Resource Bound ◽  
Multi Classification

The present paper studies a particular collection of classification problems, i.e., the classification of recursive predicates and languages, for arriving at a deeper understanding of what classification really is. In particular, the classification of predicates and languages is compared with the classification of arbitrary recursive functions and with their learnability. The investigation undertaken is refined by introducing classification within a resource bound resulting in a new hierarchy. Furthermore, a formalization of multi-classification is presented and completely characterized in terms of standard classification. Additionally, consistent classification is introduced and compared with both resource bounded classification and standard classification. Finally, the classification of families of languages that have attracted attention in learning theory is studied, too.

scholarly journals Validity of classification of distal radial fractures in the Swedish fracture register

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Malena Bergvall ◽  
Carl Bergdahl ◽  
Carl Ekholm ◽  
David Wennergren
Keyword(s):  
Gold Standard ◽  
Reference Group ◽  
Observer Agreement ◽  
Distal Radial Fractures ◽  
Young Males ◽  
Small Peak ◽  
National Quality ◽  
Fracture Register ◽  
Standard Classification

Abstract Background Distal radial fractures (DRF) are one of the most common fractures with a small peak in incidence among young males and an increasing incidence with age among women. The reliable classification of fractures is important, as classification provides a framework for communicating effectively on clinical cases. Fracture classification is also a prerequisite for data collection in national quality registers and for clinical research. Since its inception in 2011, the Swedish Fracture Register (SFR) has collected data on more than 490,000 fractures. The attending physician classifies the fracture according to the AO/OTA classification upon registration in the SFR. Previous studies regarding the classification of distal radial fractures (DRF) have shown difficulties in inter- and intra-observer agreement. This study aims to assess the accuracy of the registration of DRF in adults in the SFR as it is carried out in clinical practice. Methods A reference group of three experienced orthopaedic trauma surgeons classified 128 DRFs, randomly retrieved from the SFR, at two classification sessions 6 weeks apart. The classification the reference group agreed on was regarded as the gold standard classification for each fracture. The accuracy of the classification in the SFR was defined as the agreement between the gold standard classification and the classification in the SFR. Inter- and intra-observer agreement was evaluated and the degree of agreement was calculated as Cohen’s kappa. Results The accuracy of the classification of DRF in the SFR was kappa = 0.41 (0.31–0.51) for the AO/OTA subgroup/group and kappa = 0.48 (0.36–0.61) for the AO/OTA type. This corresponds to moderate agreement. Inter-observer agreement ranged from kappa 0.22–0.48 for the AO/OTA subgroup/group and kappa 0.48–0.76 for the AO/OTA type. Intra-observer agreement ranged from kappa 0.52–0.70 for the AO/OTA subgroup/group and kappa 0.71–0.76 for the AO/OTA type. Conclusions The study shows moderate accuracy in the classification of DRF in the SFR. Although the degree of accuracy for DRF appears to be lower than for other fracture locations, the accuracy shown in the current study is similar to that in previous studies of DRF.

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

scholarly journals MultiKOC: Multi-One-Class Classifier Based K-Means Clustering

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 134
Author(s):  
Loai Abdallah ◽  
Murad Badarna ◽  
Waleed Khalifa ◽  
Malik Yousef
Keyword(s):  
Clustering Algorithm ◽  
Main Idea ◽  
Molecular Classification ◽  
Positive Sample ◽  
Classification Problems ◽  
Multiple Cancer ◽  
Multiple Tumor ◽  
One Class Classifier ◽  
The Given

In the computational biology community there are many biological cases that are considered as multi-one-class classification problems. Examples include the classification of multiple tumor types, protein fold recognition and the molecular classification of multiple cancer types. In all of these cases the real world appropriately characterized negative cases or outliers are impractical to achieve and the positive cases might consist of different clusters, which in turn might lead to accuracy degradation. In this paper we present a novel algorithm named MultiKOC multi-one-class classifiers based K-means to deal with this problem. The main idea is to execute a clustering algorithm over the positive samples to capture the hidden subdata of the given positive data, and then building up a one-class classifier for every cluster member’s examples separately: in other word, train the OC classifier on each piece of subdata. For a given new sample, the generated classifiers are applied. If it is rejected by all of those classifiers, the given sample is considered as a negative sample, otherwise it is a positive sample. The results of MultiKOC are compared with the traditional one-class, multi-one-class, ensemble one-classes and two-class methods, yielding a significant improvement over the one-class and like the two-class performance.

scholarly journals Introduction to the History and Current Status of Evidence-Based Korean Medicine: A Unique Integrated System of Allopathic and Holistic Medicine

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Chang Shik Yin ◽  
Seong-Gyu Ko
Keyword(s):  
Evidence Based Medicine ◽  
Holistic Medicine ◽  
National Standard ◽  
Evidence Based ◽  
Korean Medicine ◽  
Classification Of Diseases ◽  
Recent Developments ◽  
Standard Classification ◽  
Based Medicine

Objectives. Korean medicine, an integrated allopathic and traditional medicine, has developed unique characteristics and has been active in contributing to evidence-based medicine. Recent developments in Korean medicine have not been as well disseminated as traditional Chinese medicine. This introduction to recent developments in Korean medicine will draw attention to, and facilitate, the advancement of evidence-based complementary alternative medicine (CAM).Methods and Results. The history of and recent developments in Korean medicine as evidence-based medicine are explored through discussions on the development of a national standard classification of diseases and study reports, ranging from basic research to newly developed clinical therapies. A national standard classification of diseases has been developed and revised serially into an integrated classification of Western allopathic and traditional holistic medicine disease entities. Standard disease classifications offer a starting point for the reliable gathering of evidence and provide a representative example of the unique status of evidence-based Korean medicine as an integration of Western allopathic medicine and traditional holistic medicine.Conclusions. Recent developments in evidence-based Korean medicine show a unique development in evidence-based medicine, adopting both Western allopathic and holistic traditional medicine. It is expected that Korean medicine will continue to be an important contributor to evidence-based medicine, encompassing conventional and complementary approaches.

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