Patent application EP18210633A (EP), filed 2018-12-06
Publication number: EP3576024A1
Title: ACCESSIBLE MACHINE LEARNING
Abstract: According to an aspect of an embodiment, a method may include obtaining a data set that includes categories (or features), and a target criteria. The method may further include obtaining a first decision tree model using the data set. The method may further include ranking the categories based on the first decision tree model and removing low-ranking categories from the data set. The method may further include generating a second decision tree model using the data set. The second decision tree model may include branch nodes. Each of branch nodes may represent a branch criteria. The method may further include pruning a branch node. The method may further include designating a remaining branch nodes as a rule node. The method may further include generating a rule based on the branch criteria of the rule node and presenting the rule in a graphical user interface.
IPC codes: G06N  20/    00            A I
G06N   5/    00            A I
Claims: number: 1
text: A method comprising:
number: 2
text: The method of claim 1, further comprising:
number: 3
text: The method of claim 1 or 2, wherein the method further comprises presenting on the display in the graphical user interface a visual representation of values of a selected category of the data set based on relationships between the values of the selected category and target values.
number: 4
text: The method of claim 1, 2 or 3, further comprising:
number: 5
text: The method of any preceding claim, wherein the rule includes a precondition based on a parent nodes of the rule node and a post-condition based on the branch criteria of the rule node.
number: 6
text: The method of any preceding claim, the method further comprising:
number: 7
text: The method of any preceding claim, the method further comprising:
number: 8
text: At least one non-transitory computer-readable media configured to store one or more instructions that when executed by at least one processor cause or direct a system to perform the method of any one of claims 1 to 7.
number: 9
text: At least one non-transitory computer-readable medium configured to store one or more instructions that when executed by at least one processor cause or direct a system to perform operations, the operations comprising:
number: 10
text: The non-transitory computer-readable medium of claim 9, wherein the operations further comprise prior to the generating the decision tree model:
number: 11
text: The non-transitory computer-readable medium of claim 9 or 10, the operations further comprising:
number: 12
text: The non-transitory computer-readable medium of claim 9, 10 or 11, wherein the operations further comprise presenting on the display in the graphical user interface a visual representation of values of a selected category of the data set based on relationships between the values of the selected category and target values.
number: 13
text: The non-transitory computer-readable medium of claim 9, 10, 11 or 12, the operations further comprising:
number: 14
text: The non-transitory computer-readable medium of any one of claims 9 to 13, wherein the rule includes a precondition based on a parent nodes of the rule node and a post-condition based on the branch criteria of the rule node.
number: 15
text: The non-transitory computer-readable medium of any one of claims 9 to 14, the operations further comprising:
number: 16
text: The non-transitory computer-readable medium of any one of claims 9 to 15, the operations further comprising:
number: 17
text: A system comprising:
Office action: search_report, dated 2019-12-04
Prior art cited: ref_id: - "Applied Predictive Modeling", 17 May 2013, SPRINGER, US, ISBN: 978-1-4614-6848-6, article MAX KUHN ET AL, pages: 19-94,173 - 502, XP055598169, DOI: 10.1007/978-1-4614-6849-3
ref_type: npl
severity: background
ref_id: - EIBE FRANK ET AL: "The WEKA Workbench", 1 October 2016 (2016-10-01), XP055598204, Retrieved from the Internet <URL:https://www.cs.waikato.ac.nz/ml/weka/Witten_et_al_2016_appendix.pdf> [retrieved on 20190620]
ref_type: npl
severity: background
ref_id: - ATHANASIOS TSAKONAS: "Plot C5.0 decision trees in R", 23 June 2017 (2017-06-23), XP055598645, Retrieved from the Internet <URL:https://web.archive.org/web/20170623124030/http://r-project-thanos.blogspot.com/2014/09/plot-c50-decision-trees-in-r.html> [retrieved on 20190621]
ref_type: npl
severity: background
ref_id: - Max Kuhn ET AL, Applied Predictive Modeling, US, Springer, (20130517), pages 19-94,173 - 502, doi:10.1007/978-1-4614-6849-3, ISBN 978-1-4614-6848-6, XP055598169 [I] 1-17 * page  25  - page  92 * * page  369  - page  399 * * page  402  - page  428 * * page  439  - page  443 *
ref_type: npl
severity: background
ref_id: - Eibe Frank ET AL, "The WEKA Workbench", (20161001), URL: https://www.cs.waikato.ac.nz/ml/weka/Witten_et_al_2016_appendix.pdf, (20190620), XP055598204 [A] 1-17 * the whole document *
ref_type: npl
severity: background
ref_id: - Athanasios Tsakonas, "Plot C5.0 decision trees in R", (20170623), URL: https://web.archive.org/web/20170623124030/http://r-project-thanos.blogspot.com/2014/09/plot-c50-decision-trees-in-r.html, (20190621), XP055598645 [A] 1-17 * the whole document *
ref_type: npl
severity: background
Final disposition: pending (2019-12-04)