111,566 research outputs found
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Explanation-based learning for diagnosis
Diagnostic expert systems constructed using traditional knowledge-engineering techniques identify malfunctioning components using rules that associate symptoms with diagnoses. Model-based diagnosis (MBD) systems use models of devices to find faults given observations of abnormal behavior. These approaches to diagnosis are complementary. We consider hybrid diagnosis systems that include both associational and model-based diagnostic components. We present results on explanation-based learning (EBL) methods aimed at improving the performance of hybrid diagnostic problem solvers. We describe two architectures called EBL_IA and EBL(p). EBL_IA is a form fo "learning in advance" that pre-compiles models into associations. At run-time the diagnostic system is purely associational. In EBL(p), the run-time diagnosis system contains associational, MBD, and EBL components. Learned associational rules are preferred but when they are incomplete they may produce too many incorrect diagnoses. When errors cause performance to dip below a give threshold p, EBL(p) activates MBD and explanation-based "learning while doing". We present results of empirical studies comparing MBD without learning versus EBL_IA and EBL(p). The main conclusions are as follows. EBL_IA is superior when it is feasible but it is not feasible for large devices. EBL(p) can speed-up MBD and scale-up to larger devices in situations where perfect accuracy is not required
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Multi-class protein fold classification using a new ensemble machine learning approach.
Protein structure classification represents an important process in understanding the associations
between sequence and structure as well as possible functional and evolutionary relationships.
Recent structural genomics initiatives and other high-throughput experiments have populated the
biological databases at a rapid pace. The amount of structural data has made traditional methods
such as manual inspection of the protein structure become impossible. Machine learning has been
widely applied to bioinformatics and has gained a lot of success in this research area. This work
proposes a novel ensemble machine learning method that improves the coverage of the classifiers
under the multi-class imbalanced sample sets by integrating knowledge induced from different base
classifiers, and we illustrate this idea in classifying multi-class SCOP protein fold data. We have
compared our approach with PART and show that our method improves the sensitivity of the
classifier in protein fold classification. Furthermore, we have extended this method to learning over
multiple data types, preserving the independence of their corresponding data sources, and show
that our new approach performs at least as well as the traditional technique over a single joined
data source. These experimental results are encouraging, and can be applied to other bioinformatics
problems similarly characterised by multi-class imbalanced data sets held in multiple data
sources
On the role of pre and post-processing in environmental data mining
The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Large, labeled datasets have driven deep learning methods to achieve
expert-level performance on a variety of medical imaging tasks. We present
CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240
patients. We design a labeler to automatically detect the presence of 14
observations in radiology reports, capturing uncertainties inherent in
radiograph interpretation. We investigate different approaches to using the
uncertainty labels for training convolutional neural networks that output the
probability of these observations given the available frontal and lateral
radiographs. On a validation set of 200 chest radiographic studies which were
manually annotated by 3 board-certified radiologists, we find that different
uncertainty approaches are useful for different pathologies. We then evaluate
our best model on a test set composed of 500 chest radiographic studies
annotated by a consensus of 5 board-certified radiologists, and compare the
performance of our model to that of 3 additional radiologists in the detection
of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the
model ROC and PR curves lie above all 3 radiologist operating points. We
release the dataset to the public as a standard benchmark to evaluate
performance of chest radiograph interpretation models.
The dataset is freely available at
https://stanfordmlgroup.github.io/competitions/chexpert .Comment: Published in AAAI 201
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Machine learning : techniques and foundations
The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples
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