Feature engineering on forest cover type data with ensemble of decision trees

Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/8058

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DC Field	Value	Language
dc.contributor.author	Pruthvi, H.R.
dc.contributor.author	Nisha, K.K.
dc.contributor.author	Chandana, T.L.
dc.contributor.author	Navami, K.
dc.contributor.author	Biju, R.M.
dc.date.accessioned	2020-03-30T10:18:02Z	-
dc.date.available	2020-03-30T10:18:02Z	-
dc.date.issued	2015
dc.identifier.citation	Souvenir of the 2015 IEEE International Advance Computing Conference, IACC 2015, 2015, Vol., , pp.1093-1098	en_US
dc.identifier.uri	http://idr.nitk.ac.in/jspui/handle/123456789/8058	-
dc.description.abstract	The paper aims to determine the forest cover type of the dataset containing cartographic attributes evaluated over four wilderness areas of Roosevelt National Forest of Northern Colorado. The cover type data is provided by US Forest service inventory, while Geographic Information System (GIS) was used to derive cartographic attributes like elevation, slope, soil type etc. Dataset was analyzed, pre processed and feature engineering techniques were applied to derive relevant and non-redundant features. A comparative study of various decision tree algorithms namely, CART, C4.5, C5.0 was performed on the dataset. With the new dataset built by applying feature engineering techniques, Random Forest and C5.0 improved the accuracy by 9% compared to the raw dataset. � 2015 IEEE.	en_US
dc.title	Feature engineering on forest cover type data with ensemble of decision trees	en_US
dc.type	Book chapter	en_US
Appears in Collections:	2. Conference Papers

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