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oai:mountainscholar.org:10217/2441012026-04-23T11:02:49Zcom_10217_100532com_10217_100000com_10217_100388com_10217_100303col_10217_100538col_10217_100389

Bins Filho, José Carlos, author Draper, Bruce A., advisor Kirby, Michael, committee member Beveridge, J. Ross, committee member Anderson, Charles W., committee member 2026-04-22T18:19:07Z 2000 https://hdl.handle.net/10217/244101 https://doi.org/10.25675/3.026725 Most learning systems use hand-picked sets of features as input data for their learning algorithms. This is particularly true of computer vision systems, where the number of features that can be computed over an image is, for practical purposes, limitless. Unfortunately, most of these features are irrelevant or redundant to a given task, and no feature selection algorithm to date can handle such large feature sets. Moreover, many standard feature selection algorithms perform poorly when faced with many irrelevant and redundant features. This work addresses the feature selection problem by proposing a three-step algorithm. The first step uses an algorithm based on the well known algorithm called Relief [54] to remove irrelevance: the second step clusters features using K-means to remove redundancy: and the third step is a standard feature selection algorithm. This three-step algorithm is shown to be more effective than standard feature selection algorithms for data with lots of irrelevance and redundancy. In other experiment a data set with 4096 features was reduced to 5% of its original size with very little information loss. In addition, we modify Relief to remove its bias against non-monotonic features and use correlation as the distance measure for K-means. doctoral dissertations English eng Colorado State University. Libraries 2000-2019 Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. Per the terms of a contractual agreement, all use of this item is limited to the non-commercial use of Colorado State University and its authorized users. computer science Feature selection from huge feature sets in the context of computer vision Text Doctor of Philosophy (Ph.D.) Doctoral Computer Science Colorado State University This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). open.access