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Rule Fitness and Pathology in Learning Classifier Systems

Tim Kovacs
2004 Evolutionary Computation  
It has long been known that in some relatively simple reinforcement learning tasks traditional strength-based classifier systems will adapt poorly and show poor generalisation.  ...  We distinguish between strong and fit overgeneral rules, and show that although strong overgenerals are fit in a strength-based system called SB-XCS, they are not in XCS.  ...  Introduction When applied to reinforcement learning, Learning Classifier Systems (LCS) evolve sets of rules in order to maximise the return they receive from their task environment.  ... 
doi:10.1162/106365604773644341 pmid:15096307 fatcat:amt7ba5io5dp3nx6psfiokf24y

Rule Fitness and Pathology in Learning Classifier Systems

Tim Kovacs
2004 Evolutionary Computation  
It has long been known that in some relatively simple reinforcement learning tasks traditional strength-based classifier systems will adapt poorly and show poor generalisation.  ...  We distinguish between strong and fit overgeneral rules, and show that although strong overgenerals are fit in a strength-based system called SB-XCS, they are not in XCS.  ...  Introduction When applied to reinforcement learning, Learning Classifier Systems (LCS) evolve sets of rules in order to maximise the return they receive from their task environment.  ... 
doi:10.1162/evco.2004.12.1.99 pmid:15096307 fatcat:4th7w4gggffevkv73gaj2dvw2u

Rule Fitness and Pathology in Learning Classifier Systems [chapter]

Tim Kovacs
Foundations of Learning Classifier Systems  
It has long been known that in some relatively simple reinforcement learning tasks traditional strength-based classifier systems will adapt poorly and show poor generalisation.  ...  We distinguish between strong and fit overgeneral rules, and show that although strong overgenerals are fit in a strength-based system called SB-XCS, they are not in XCS.  ...  Introduction When applied to reinforcement learning, Learning Classifier Systems (LCS) evolve sets of rules in order to maximise the return they receive from their task environment.  ... 
doi:10.1007/11319122_9 fatcat:m2jvsd2lynhmpmqpmaz7h4znnu

Automatic Classification of Cancer Pathology Reports: A Systematic Review

Thiago Santos, Amara Tariq, Judy Wawira Gichoya, Hari Trivedi, Imon Banerjee
2022 Journal of Pathology Informatics  
We benchmarked the systems based on methodology, complexity of the prediction task and core types of NLP models: i) Rule-based and Intelligent systems, ii) statistical machine learning, and iii) deep learning  ...  classifying pathology reports published between the years of 2010 and 2021.  ...  machine learning, and rule-based systems.  ... 
doi:10.1016/j.jpi.2022.100003 pmid:35242443 pmcid:PMC8860734 fatcat:c5hve3ottnb6hbcalimya6u3ae

Observer-invariant histopathology using genetics-based machine learning

Xavier Llorà, Anusha Priya, Rohit Bhargava
2007 Natural Computing  
Uniting spectroscopic imaging data and computeraided diagnoses (CADx), we seek to provide a new approach to pathology by automating the recognition of cancer in complex tissue.  ...  This paper proposes and validates and efficient GBML technique-NAX-based on an incremental genetics-based rule learner that exploits massive parallelisms-via the message passing interface (MPI)-and efficient  ...  Thanks also to Kumara Sastry for always being ready for a hallway discussion and to the Automated Learning Group at the National Center for Supercomputing Applications for hosting this joint collaboration  ... 
doi:10.1007/s11047-007-9056-6 fatcat:6rumjgyervd6lgqe2vigjbn24y

Comparative analysis of selected classifiers in posterior cruciate ligaments computer aided diagnosis

P. Zarychta, P. Badura, E. Pietka
2017 Bulletin of the Polish Academy of Sciences: Technical Sciences  
Among the classifiers we introduce and specify the particle swarm optimization based Sugeno-type fuzzy inference system and compare its performance to other established classification systems.  ...  The classification accuracy metrics: sensitivity, specificity, and Dice index all exceed 90% for each classifier under consideration, indicating high level of the proposed feature vector relevance in the  ...  During the PSO training the original ANFIS is subjected to changes, also in terms of the number and formulae of fuzzy rules. 3.7. Classifier training and testing procedure.  ... 
doi:10.1515/bpasts-2017-0008 fatcat:o76nrj3zwffnhcz4fvhc27g4jm

A classifier based on the artificial neural network approach for cardiologic auscultation in pediatrics

Sanjay R. Bhatikar, Curt DeGroff, Roop L. Mahajan
2005 Artificial Intelligence in Medicine  
We were able to achieve classification accuracy of 83% sensitivity and 90% specificity in discriminating between innocent and pathological heart murmurs.  ...  These are: the smartthreshold setting rule, the committee of experts' scheme, and the knowledge-access optimization scheme.  ...  In addition to the TCH pilot grant, this work was supported by NIH T35 Training Grant No. 2 T35 DK07496-16 and the American Heart Association Desert Mountain Affiliate Grant.  ... 
doi:10.1016/j.artmed.2004.07.008 pmid:15811789 fatcat:6ndijyjqyfaprjokmusa5ypxky

Enhanced Image-Based Endoscopic Pathological Site Classification Using an Ensemble of Deep Learning Models

Dat Tien Nguyen, Min Beom Lee, Tuyen Danh Pham, Ganbayar Batchuluun, Muhammad Arsalan, Kang Ryoung Park
2020 Sensors  
With the advances in technology and image processing techniques, computer-aided diagnosis (CAD) systems have been developed and applied in several medical systems to assist doctors in diagnosing diseases  ...  pathological site classification using a CAD system as compared to the state-of-the-art methods.  ...  networks and three systems based on three combination rules, as shown in Figure 8 .  ... 
doi:10.3390/s20215982 pmid:33105736 fatcat:cwbyp3but5dxtbrq64b7mrsvvi

Towards a Theory of Strong Overgeneral Classifiers [chapter]

Tim Kovacs
2001 Foundations of Genetic Algorithms 6  
We analyse the concept of strong overgeneral rules, the Achilles' heel of traditional Michigan-style learning classifier systems, using both the traditional strength-based and newer accuracy-based approaches  ...  Finally, we distinguish fit overgeneral rules, show how strength and accuracy-based fitness differ in their response to fit overgenerals and conclude by considering possible extensions to this work. £  ...  Acknowledgements Thank you to Manfred Kerber and the anonymous reviewers for comments, and to the organisers for their interest in classifier systems.  ... 
doi:10.1016/b978-155860734-7/50092-5 dblp:conf/foga/Kovacs00 fatcat:6rkyxo5z7reddlmi35x2wxluda

Self-integrating knowledge-based brain tumor diagnostic system

Ching-Hung Wang, Tzung-Pei Hong, Shian-Shyong Tseng
1996 Expert systems with applications  
In this paper, we present a self-integrating knowledge-based expert system for brain tumor diagnosis. The system we propose comprises knowledge building, knowledge inference and knowledge refinement.  ...  to construct an initial knowledge base, thus eliminating a major bottleneck in developing a brain tumor diagnostic system.  ...  Accuracy and complexity are then combined to represent the fitness value of the rule set.  ... 
doi:10.1016/s0957-4174(96)00050-4 fatcat:wbbetldhvzdavo5cm56kmabgwi

Rule-based categorization deficits in focal basal ganglia lesion and Parkinson's disease patients

Shawn W. Ell, Andrea Weinstein, Richard B. Ivry
2010 Neuropsychologia  
Patients with basal ganglia (BG) pathology are consistently found to be impaired on rule-based category learning tasks in which learning is thought to depend upon the use of an explicit, hypothesis-guided  ...  These data suggest that the demands on selective attention and working memory affect the presence of impairment in patients with focal BG lesions and the nature of the impairment in patients with PD.  ...  Thanks to Ed Drasby, Donatella Scabini, Leslie Shupenko, and William Stamey for their assistance in the recruitment and/or assessment of the patients.  ... 
doi:10.1016/j.neuropsychologia.2010.06.006 pmid:20600196 pmcid:PMC2914131 fatcat:bbf6kme5lvbztfaegmpot5leue

Natural language processing systems for pathology parsing in limited data environments with uncertainty estimation

Anobel Y Odisho, Briton Park, Nicholas Altieri, John DeNero, Matthew R Cooperberg, Peter R Carroll, Bin Yu
2020 JAMIA Open  
We aim to improve uncertainty estimates of machine learning-based pathology parsers and evaluate performance in low data settings.  ...  Conclusions We find that when applying machine learning to pathology parsing, large datasets may not always be needed, and that calibration methods can improve the reliability of uncertainty estimates.  ...  AUTHOR CONTRIBUTIONS BP and NA implemented, tested, and validated the experiments. All authors were involved in designing and developing the study and writing the paper.  ... 
doi:10.1093/jamiaopen/ooaa029 pmid:33381748 pmcid:PMC7751177 fatcat:l2sxnhvpojg5zb3tsrfnesmf74

An artificial intelligence natural language processing pipeline for information extraction in neuroradiology [article]

Henry Watkins, Robert Gray, Ashwani Jha, Parashkev Nachev
2021 arXiv   pre-print
Our pipeline uses a hybrid sequence of rule-based and artificial intelligence models to accurately extract and summarise neurological reports.  ...  Extracting information within reports and summarising patient presentations in a way amenable to downstream analysis would be enormously beneficial for operational and clinical research.  ...  These tasks are a mixture of rule-based, deep learning, and traditional machine-learning algorithms. In this section, we describe the deep learning specific tasks of the pipeline.  ... 
arXiv:2107.10021v1 fatcat:czyujlwkvnaejltvpcigxu5p2m

Handgrip Strength Evaluation Using Neuro Fuzzy Approach

Woo Chaw Seng, Mahsa Chitsaz
2010 Malaysian Journal of Computer Science  
The expert rules define the membership function for the fuzzy system. The fuzzy model based on the membership function, fed in by the neural network will intelligently classify the data.  ...  The neurofuzzy analysis provides system identification and interpretability of fuzzy models and learning capability of neural networks.  ...  Acknowledgment We would like to thank Ahmad Hafiz for doing this work as his thesis and implementing the application in a user friendly way.  ... 
doi:10.22452/mjcs.vol23no3.3 fatcat:e3cag43kzfdjdfattcpeq4g5mu

Study On Cardiovascular Disease Classification Using Machine Learning Approaches

R. Subha
2016 International Journal Of Engineering And Computer Science  
Machine learning based method is used to classify between healthy people and people with disease.  ...  The diagnosis of heart disease which depends in most cases on complex grouping of clinical and pathological data.  ...  The automatic procedure to generate the fuzzy rules is an advantage of the proposed system and the weighted procedure introduced in the proposed work is additional advantage for effective learning of the  ... 
doi:10.18535/ijecs/v4i12.16 fatcat:lbr4cifasramhnnpunr2ozmn6a
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