Data Mining for Molecules with 2-D Neural Network Sensitivity Analysis

Autor:	F. Arciniegas, Robert Kewley, Mark J. Embrechts, Muhsin Özdemir
Rok vydání:	2003
Předmět:	Set (abstract data type) Data records Artificial neural network Surface mining Computer science Feature selection Sensitivity (control systems) Data mining computer.software_genre computer Software
Zdroj:	International Journal of Smart Engineering System Design. 5:225-239
ISSN:	1607-8500 1025-5818
Popis:	This paper illustrates a data mining application using two-dimensional (2-D) neural network sensitivity analysis for gaining insight into data strip mining problems. Data strip mining refers to predictive data mining problems where there are a large number of descriptive features, and the number of features is on the order of or exceeds the number of data records (e.g., 100 to 1000 features for 50 to 300 data records). After reducing the number of descriptive features to a manageable set using 1-D neural network sensitivity analysis (e.g., 40 features), a 2-D neural network sensitivity analysis allows the user to visualize variations in the response to identify relevant combinations of features. Each relevant combination can then be analyzed independently to look for interesting patterns and relationships, and can be used in this way to either prune more features or to get insight into the underlying rules for the model. 2-D sensitivity analysis enables the exploration of relevant relationships and featur...
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ff7a93d0c21b6fe00bee5a051d6e400b https://doi.org/10.1080/10255810390245555 Zobrazit plný text záznamu Plný text ve formátu PDF