US 2019/0377771 A1 · SYSTEM AND METHOD OF PRE-PROCESSING DISCRETE DATASETS FOR USE IN MACHINE LEARNING

Abstract

There is provided a system and method of pre-processing discrete datasets for use in machine learning. The method includes: determining a median and a standard deviation of an input discrete dataset; determining a probability mass function including a probability of finding a particular data point in the input discrete dataset within a particular bin of a histogram representative of the input discrete dataset; transforming the probability mass function into a continuously differentiable probability density function using the standard deviation, the probability density function determined using a parametric control function, the parametric control function including a lognormal derivative of the probability density function, the parameters within the control function are estimated using optimization that minimizes a mean-squared error of an objective function; and outputting the probability density function for use an input to a machine learning model.

Claims20

Dependent claims

A method of pre-processing discrete datasets for use in machine learning, the method executable on one or more computer processors, the method comprising:

receiving an input discrete dataset;
determining a median and a standard deviation of the input discrete dataset;
generating a probability mass function comprising a probability of finding a particular data point in the input discrete dataset within a particular bin of a histogram representative of the input discrete dataset;
transforming the probability mass function into a continuously differentiable probability density function using the standard deviation, the probability density function determined using a parametric control function, the parametric control function comprising a lognormal derivative of the probability density function, the parameters within the control function are estimated using optimization that minimizes a mean-squared error of an objective function;
and
outputting the probability density function for use an input to a machine learning model.

3 more elements

A system of pre-processing discrete datasets for use in machine learning, the system comprising one or more processors and one or more non-transitory computer storage media, the one or more non-transitory computer storage media causing the one or more processors to execute:

an input module to receive an input discrete dataset;
a compression module to:
determine a median and a standard deviation of the input discrete dataset;
determine a probability mass function comprising a probability of finding a particular data point in the input discrete dataset within a particular bin of a histogram representative of the input discrete dataset;
and
transform the probability mass function into a continuously differentiable probability density function using the standard deviation, the probability density function determined using a parametric control function, transforming the probability mass function into a continuously differentiable probability density function using the standard deviation, the probability density function determined using a parametric control function, the parametric control function comprising a lognormal derivative of the probability density function, the parameters within the control function are estimated using optimization that minimizes a mean-squared error of an objective function, the parameters within the control function are estimated using optimization that minimizes a mean-squared error;
and
an output module to output the probability density function for use an input to a machine learning model.

5 more elements

Dependencies are inferred from the claim text.

System and method of pre-processing discrete datasets for use in machine learning

Life of this patent

System and method of pre-processing discrete datasets for use in machine learning

Life of this patent

Abstract

Claims20