US 11,893,506 B1
Decision tree training with difference subsets of training samples based on a plurality of classifications
Xiangdong Wang, Carlisle, MA (US); Yibo Liu, Boxborough, MA (US); and Peter L. Chu, Lexington, MA (US)
Assigned to Hewlett-Packard Development Company, L.P., Spring, TX (US)
Filed by Hewlett-Packard Development Company, L.P., Spring, TX (US)
Filed on Jun. 23, 2020, as Appl. No. 16/909,644.
Application 16/909,644 is a continuation of application No. 16/896,818, filed on Jun. 9, 2020, abandoned.
Int. Cl. G06N 5/00 (2023.01); G06N 5/04 (2023.01); G06N 20/20 (2019.01); G06N 5/01 (2023.01)
CPC G06N 5/04 (2013.01) [G06N 5/01 (2023.01); G06N 20/20 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
obtaining a plurality of training samples with a plurality of classifications that include a first classification and a second classification;
training an initial tree with an initial set of training samples selected from the plurality of training samples using an initial set of feature values extracted from the set of training samples, wherein the initial set of training samples correspond to the plurality of classifications; and
in response to determining that the initial tree incorrectly classified the initial set of training samples at an output node of the initial tree,
selecting a subsequent set of training samples from the plurality of training samples, and
training a subsequent tree using a subsequent set of feature values extracted from the subsequent set of training samples, the subsequent tree comprising a root node that replaces the output node of the initial tree that incorrectly classified the initial set of training samples,
wherein the subsequent set of training samples correspond to the plurality of classifications, and
wherein the subsequent set of training samples is different from the initial set of training samples.