Mining Diversified Decision Trees Across Multiple Datasets to Capture Similarities and Alignable Differences

Bibliographic Details
Main Author:	Han, Qian
Language:	English
Published:	Wright State University / OhioLINK 2013
Subjects:	Computer Science
Online Access:	http://rave.ohiolink.edu/etdc/view?acc_num=wright1376511749

id	ndltd-OhioLink-oai-etd.ohiolink.edu-wright1376511749
record_format	oai_dc
spelling	ndltd-OhioLink-oai-etd.ohiolink.edu-wright13765117492021-08-03T06:19:10Z Mining Diversified Decision Trees Across Multiple Datasets to Capture Similarities and Alignable Differences Han, Qian Computer Science This dissertation studies the problem of mining shared and alignable difference knowledge structures across multiple datasets/applications. Shared and alignable difference knowledge structures are important for identifying analogies between application domains and for forming new hypothesis in challenging research applications, and for assessing the degree and types of knowledge-level similarities and differences between application domains for use in learning transfer.Generally speaking, shared knowledge structures characterize underlying datasets and highlight conceptual-level structural similarities among the datasets. This dissertation studies the mining of shared decision trees, which are a special type of shared knowledge structures. We first consider building one shared decision tree with high classification accuracy and high data distribution similarity for two given datasets. Moreover, it is observed that one shared decision tree may only present a limited view of shared behaviors between two given datasets. In order to help users to select from multiple diversified perspectives on shared knowledge structures, we propose the diversified decision tree set mining problem, whose goal is to mine a small set of k diversified high quality shared decision trees. Besides requiring each tree in the set to have high classification accuracy and highly similar data distributions in the given datasets, different trees in the set are also required to behighly different from each other. Algorithms are developed to solve both problems. Experimental results on microarray datasets for medicine are reported to evaluate the algorithms, together with the mined shared decision trees.This dissertation also introduces and studies the mining of alignable differences. Roughly speaking, alignable difference knowledge structures indicate significant differences in the context of a large amount of similarities among two given datasets. This dissertation considers alignable differences in the form of cross-domain decision trees. An algorithm to solve this problem is presented. Experimental results on microarray datasets for medicine are reported to evaluate the algorithm. 2013-08-16 English text Wright State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=wright1376511749 http://rave.ohiolink.edu/etdc/view?acc_num=wright1376511749 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection	NDLTD
language	English
sources	NDLTD
topic	Computer Science
spellingShingle	Computer Science Han, Qian Mining Diversified Decision Trees Across Multiple Datasets to Capture Similarities and Alignable Differences
author	Han, Qian
author_facet	Han, Qian
author_sort	Han, Qian
title	Mining Diversified Decision Trees Across Multiple Datasets to Capture Similarities and Alignable Differences
title_short	Mining Diversified Decision Trees Across Multiple Datasets to Capture Similarities and Alignable Differences
title_full	Mining Diversified Decision Trees Across Multiple Datasets to Capture Similarities and Alignable Differences
title_fullStr	Mining Diversified Decision Trees Across Multiple Datasets to Capture Similarities and Alignable Differences
title_full_unstemmed	Mining Diversified Decision Trees Across Multiple Datasets to Capture Similarities and Alignable Differences
title_sort	mining diversified decision trees across multiple datasets to capture similarities and alignable differences
publisher	Wright State University / OhioLINK
publishDate	2013
url	http://rave.ohiolink.edu/etdc/view?acc_num=wright1376511749
work_keys_str_mv	AT hanqian miningdiversifieddecisiontreesacrossmultipledatasetstocapturesimilaritiesandalignabledifferences
_version_	1719434743390404608

Mining Diversified Decision Trees Across Multiple Datasets to Capture Similarities and Alignable Differences

Similar Items