Knowledge Discovery using Machine Learning Algorithms - Sujata Dash,Bichitrananda Patra
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Piegāde 12-18 darba dienu laikā
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The goal of this book is to provide a more effective way to extract features with highly important information to a specific disease, i.e. informative features, using correlation based rough set feature extraction method (RSs), rough set, genetic algorithms (GAs) and its variants, fuzzy-rough set, nearest neighbor, decision tree algorithms and partial least square method and some adaptive neural networks du ... Pilns apraksts
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Aprašymas
The goal of this book is to provide a more effective way to extract features with highly important information to a specific disease, i.e. informative features, using correlation based rough set feature extraction method (RSs), rough set, genetic algorithms (GAs) and its variants, fuzzy-rough set, nearest neighbor, decision tree algorithms and partial least square method and some adaptive neural networks due to their learning abilities to construct hypotheses that can explain complex relationships in the data. This research explores the effectiveness of integrated and hybrid feature extraction methods proposed in the following chapters, in analyzing gene expression activities, based on a specific tumor disease and identifying the informative genes that underlie different precision levels in the extraction process. The identified gene subset may give an enhanced insight on the gene-gene interaction in response to different stages of abnormal cell growth which could be vital in designing treatment strategies to prevent any progression of abnormal cells.
Vairāk informācijas
| Autors | Sujata Dash, Bichitrananda Patra |
|---|---|
| Izdevējs | LAP LAMBERT Academic Publishing |
| Izlaides gads | 2016 |
| Vāka tips | Mīkstais vāks |
| EAN | 9783659926204 |