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1.发明申请
公开(公告)号：WO2022000039A1
公开(公告)日：2022-01-06
申请号：PCT/AU2021/050703
申请日：2021-06-30
Inventor： DU PREEZ, Warren
Abstract: A method for generating an artificial intelligence model for determining probability of rainfall, by applying a decision tree ensemble learning process on a dataset, the method comprising: receiving a first dataset comprising at least two variables; determining at least one split criteria for each variable within the first dataset; partitioning the first dataset based on each determined split criteria; calculating a measure of directionality for each partition of data; performing a constrained node selection process by selecting a candidate variable and split criteria, wherein the selection is made to keep a consistent directionality for the selected variable based on existing nodes; updating a directionality table at the end of a constrained node selection; reiterating the constrained node selection process for every node selection throughout the decision tree ensemble learning process until an ensemble model is generated; and processing a second dataset with the generated ensemble model to determine probability of rainfall; wherein the first dataset contains data received from one or more sensors, the received data including data pertaining to temperature.
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公开(公告)号：WO2021207797A8
公开(公告)日：2021-10-21
申请号：PCT/AU2021/050342
申请日：2021-04-16
Inventor： DU PREEZ, Warren
IPC: G06N20/00 , G06F17/18 , G06Q10/04 , G06F16/906 , G01W1/00 , G01W1/14 , G06F16/285 , G06N20/20 , G06N3/08 , G06N5/003 , G06N7/005
Abstract: Embodiments generally relate to a method for selecting hybrid variables. The method comprises sampling at least one interaction effect structure of at least one multivariable dataset, sampling at least one hybrid variable for each sampled interaction effect structure, calculating a lift value for each sampled hybrid variable, and comparing the lift value to a threshold lift criteria, labelling each sampled hybrid variable based on determining that the lift value of the sample hybrid variable exceeds the threshold lift criteria, training a machine learning model to predict the likelihood of a hybrid variable having a lift which exceeds the threshold lift criteria, applying the trained machine learning model to each hybrid variable within each sampled interaction effect structure to determine a value corresponding to the likelihood of each hybrid variable having a lift which exceeds the threshold lift criteria, and retaining only hybrid variables with a likelihood value that exceeds a decision criteria. The training of the machine learning model is performed using the labelled sampled hybrid variables.
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公开(公告)号：WO2021207797A1
公开(公告)日：2021-10-21
申请号：PCT/AU2021/050342
申请日：2021-04-16
Inventor： DU PREEZ, Warren
IPC: G06N20/00 , G06F17/18 , G06Q10/04 , G06F16/906 , G01W1/00
Abstract: Embodiments generally relate to a method for selecting hybrid variables. The method comprises sampling at least one interaction effect structure of at least one multivariable dataset, sampling at least one hybrid variable for each sampled interaction effect structure, calculating a lift value for each sampled hybrid variable, and comparing the lift value to a threshold lift criteria, labelling each sampled hybrid variable based on determining that the lift value of the sample hybrid variable exceeds the threshold lift criteria, training a machine learning model to predict the likelihood of a hybrid variable having a lift which exceeds the threshold lift criteria, applying the trained machine learning model to each hybrid variable within each sampled interaction effect structure to determine a value corresponding to the likelihood of each hybrid variable having a lift which exceeds the threshold lift criteria, and retaining only hybrid variables with a likelihood value that exceeds a decision criteria. The training of the machine learning model is performed using the labelled sampled hybrid variables.
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