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, the adaptation of such methods for unsupervised learning (clustering) is investigated. We adopt an algorithm that iterates … between clustering (assuming that the number of clusters is unknown a priori) and feature selection. From this standpoint, two …
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This paper investigates the use of Design of Experiments in observational studies in order to select informative observations and features for classification. D-optimal plans are searched for in existing data and based on these plans the variables most relevant for classification are determined....
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Interactions of single nucleotide polymorphisms (SNPs) are assumed to be responsible for complex diseases such as sporadic breast cancer. Important goals of studies concerned with such genetic data are thus to identify combinations of SNPs that lead to a higher risk of developing a disease and...
Persistent link: https://www.econbiz.de/10010296746
This research article attempts to provide a recent survey on neuro-fuzzy approaches for feature selection and classification. Feature selection acts as a catalyst in reducing computation time and dimensionality, enhancing prediction performance or accuracy and curtailing irrelevant or redundant...
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Disease prediction is often characterized by a high degree of fuzziness and uncertainty. This may reside in the imperfect and complex nature of symptoms that aids in diagnosis.. For precise rice disease diagnosis, domain knowledge of expertise pathologists along with clinically screened database...
Persistent link: https://www.econbiz.de/10012042752
Traditional financial reporting usually ignores intangible assets, even though these assets play an increasingly important role in today's knowledge-based economy. As such, the valuation of intangible assets, while typically overlooked in traditional reporting, has nonetheless garnered...
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