21.11 Remove Numbers
## hybrid weighted random forests for
## classifying very high-dimensional data
## baoxun xu , joshua zhexue huang , graham williams and
## yunming ye
##
##
## department of computer science, harbin institute of technology shenzhen graduate
## school, shenzhen , china
##
## shenzhen institutes of advanced technology, chinese academy of sciences, shenzhen
## , china
## email: amusing gmail.com
## random forests are a popular classification method based on an ensemble of a
## single type of decision trees from subspaces of data. in the literature, there
## are many different types of decision tree algorithms, including c., cart, and
## chaid. each type of decision tree algorithm may capture different information
## and structure. this paper proposes a hybrid weighted random forest algorithm,
## simultaneously using a feature weighting method and a hybrid forest method to
## classify very high dimensional data. the hybrid weighted random forest algorithm
## can effectively reduce subspace size and improve classification performance
## without increasing the error bound. we conduct a series of experiments on eight
## high dimensional datasets to compare our method with traditional random forest
## methods and other classification methods. the results show that our method
## consistently outperforms these traditional methods.
## keywords: random forests; hybrid weighted random forest; classification; decision tree;
##
....Numbers may or may not be relevant to our analyses. This transform can remove numbers simply.
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