Knowledge-based neurocomputing in medicine

Cloete, Ian; Rohr, Karl

doi:10.1016/s0933-3657(03)00054-x

Cited by 12 publications

(16 citation statements)

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“…In this approach, the chromosome component value of one represents inclusion of the particular feature in the input variable set and the value of zero-deletion of the particular feature from the actual set. The GA consists of selecting parents for reproduction, performing crossover with the parents, and applying the operation of mutation to the bits representing children (Goldberg, 2013;Cloete and Zurada, 2000). Each chromosome is associated with the input vector x of the components used as the explanatory variables to the predictor.…”

Section: Feature Selection Using the Gamentioning

confidence: 99%

Data mining methods for prediction of air pollution

Siwek

Osowski

2016

International Journal of Applied Mathematics and Computer Science

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The paper discusses methods of data mining for prediction of air pollution. Two tasks in such a problem are important: generation and selection of the prognostic features, and the final prognostic system of the pollution for the next day. An advanced set of features, created on the basis of the atmospheric parameters, is proposed. This set is subject to analysis and selection of the most important features from the prediction point of view. Two methods of feature selection are compared. One applies a genetic algorithm (a global approach), and the other-a linear method of stepwise fit (a locally optimized approach). On the basis of such analysis, two sets of the most predictive features are selected. These sets take part in prediction of the atmospheric pollutants PM10, SO2, NO2 and O3. Two approaches to prediction are compared. In the first one, the features selected are directly applied to the random forest (RF), which forms an ensemble of decision trees. In the second case, intermediate predictors built on the basis of neural networks (the multilayer perceptron, the radial basis function and the support vector machine) are used. They create an ensemble integrated into the final prognosis. The paper shows that preselection of the most important features, cooperating with an ensemble of predictors, allows increasing the forecasting accuracy of atmospheric pollution in a significant way.

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Section: Feature Selection Using the Gamentioning

confidence: 99%

Data mining methods for prediction of air pollution

Siwek

Osowski

2016

International Journal of Applied Mathematics and Computer Science

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“…It is stated that Knowledge-based neurocomputing (KBN) concerns with methods to address the explicit representation and processing of knowledge where a neurocomputing system is involved (Ian Cloete and Jacek M. Zurada, 2000).…”

Section: Research Backgroundmentioning

confidence: 99%

Correlating and Cross-linking Knowledge Threads in Informledge System for Creating New Knowledge

Nair¹,

Malhotra²

2012

Proceedings of the International Conference on Knowledge Engineering and Ontology Development

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Abstract:There has been a considerable advance in computing, to mimic the way in which the brain tries to comprehend and structure the information to retrieve meaningful knowledge. It is identified that neuronal entities hold whole of the knowledge that the species makes use of. We intended to develop a modified knowledge based system, termed as Informledge System (ILS) with autonomous nodes and intelligent links that integrate and structure the pieces of knowledge. We conceive that every piece of knowledge is a cluster of cross-linked and correlated structure. In this paper, we put forward the theory of the nodes depicting concepts, referred as Entity Concept State which in turn is dealt with Concept State Diagrams (CSD). This theory is based on an abstract framework provided by the concepts. The framework represents the ILS as the weighted graph where the weights attached with the linked nodes help in knowledge retrieval by providing the direction of connectivity of autonomous nodes present in knowledge thread traversal. Here for the first time in the process of developing Informledge, we apply tenor computation for creating intelligent combinatorial knowledge with cross mutation to create fresh knowledge which looks to be the fundamentals of a typical thought process.

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“…Craven and Shavlik describe another descision tree-based rule extraction technique that uses a neural network as an oracle to provide a body of artificial examples to feed a comprehensible learner [6]. Finally, a third method for extracting decision trees is described by Schmitz et al in a paper appearing in [4], their method has the advantage of working equally well for problems involving continuous and symbolic outputs. Clearly, these techniques would also work for an ensemble of neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…An example of such an approach is described by Sima [20] and is reviewed by Cloete and Zurada [4]. That system (EXPSYS) can be queried during execution and the user is provided with the relative influence (expressed as a percent) of selected inputs in arriving at the conclusion.…”

Section: Introductionmentioning

confidence: 99%