Decision tree training with difference subsets of training samples based on a plurality of classifications
AssigneeHewlett-Packard Development Company, L.P.
InventorsXiangdong Wang, Yibo Liu, Peter L. Chu
Life of this patent
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Abstract
A method may include 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, 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, training a subsequent tree using a subsequent set of feature values extracted from a subsequent set of training samples.
Claims20
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.
A system comprising:
- a processor;
- a memory;
- and
- the memory comprising an application, wherein the application executes on the processor, uses the memory, and is configured for:
- classifying a sample to generate a sample classification with a machine learning model, the machine learning model comprising a decision tree that comprises an initial tree and a subsequent tree, wherein the machine learning model is trained by:
- 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,
- wherein the subsequent tree comprises 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.
A set of one or more non-transitory computer readable mediums comprising computer readable program code for:
- 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,
- wherein the subsequent tree comprises a root node that replaces the output node of the initial tree that incorrectly classified the initial set of training samples, the subsequent set of training samples in the plurality 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.
The set of one or more non-transitory computer readable mediums of claim further comprising computer readable program code for:
- training the subsequent tree with a subsequent first portion of the plurality of training samples corresponding to the first classification and a subsequent second portion of the plurality of training samples corresponding to the second classification, wherein the subsequent first portion is disjoint from the subsequent second portion.
The set of one or more non-transitory computer readable mediums of claim further comprising computer readable program code for:
- classifying a sample to generate a sample classification with a machine learning model, the machine learning model comprising a decision tree that comprises the initial tree and the subsequent tree.
Dependencies are inferred from the claim text.