US 2019/0318266 A1Published application

Two-class classification method for predicting class to which specific item belongs, and computing device using same

AssigneeBioInfra Life Science Inc.

InventorsChul Woo KIM, Yong Dai KIM, Kyung Nam KANG, Oh Ran KWON

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Abstract

A computing device of the present invention estimates an unknown parameter β of a model formula when distributed sample data is acquired, wherein when an estimated quantity of β is acquired, a function g is estimated using a random forest model, such that when an estimated quantity of g is acquired, the estimated quantity of g and the estimated quantity of β are used so as estimate a function G as a prediction formula for new data corresponding to a specific item such that an estimated quantity of G is acquired, and thus new data xnew is received, thereby enabling the class of the specific item to be classified from the calculated value.

Claims18

Dependent claims

A method for 2-class classification to estimate a specific class to which a specific item belongs, comprising steps of:

(a) a computing device acquiring sample data (Y1, X1), . . . , (Yn, Xn) which is independently and identically distributed, wherein n is the number of the sample data, Xi=(1, Xi1, . . . , Xid)T∈X⊂Rd is a d-dimensional vector, Yi has or is manipulated to have a value of −1 or 1, and −1 and 1 are set as respectively corresponding to two of classes;
(b) the computing device, if the sample data is acquired, estimating or supporting another device to estimate an unknown parameter β of a model equation
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wherein β=(β0, β1, . . . , βd), X is an independent variable, Y is a dependent variable, and the function g takes a form of a random forest model;
(c) the computing device, if an estimator {circumflex over (β)} of the unknown parameter β is acquired, estimating or supporting another device to estimate the function g by using the random forest model;
and
(d) the computing device, if an estimator ĝ of the function g is acquired, estimating or supporting another device to estimate the function G as an estimating equation for new data corresponding to the specific item by using the estimator {circumflex over (β)} of the unknown parameter β and the estimator ĝ of the function g, to thereby acquire an estimator Ĝ of the function G.

109 more elements

A computing device for 2-class classification to estimate a specific class to which a specific item belongs, comprising:

a communication part for acquiring sample data (Y1, X1), . . . , (Yn, Xn) which is independently and identically distributed, wherein n is the number of the sample data, Xi=(1, Xi1, . . . , Xid)T∈X⊂Rd is a d-dimensional vector, Yi has or is manipulated to have a value of −1 or 1, and −1 and 1 are set as respectively corresponding to two of classes;
and
a processor for, (i) if the sample data is acquired, estimating or supporting another device to estimate an unknown parameter β of a model equation
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wherein β=(β0, β1, . . . , βd), X is an independent variable, Y is a dependent variable, and the function g takes a form of a random forest model, (ii) if an estimator {circumflex over (β)} of the unknown parameter β is acquired, estimating or supporting another device to estimate the function g by using the random forest model, and (iii) if an estimator ĝ of the function g is acquired, estimating or supporting another device to estimate the function G as an estimating equation for new data corresponding to the specific item by using the estimator {circumflex over (β)} of the unknown parameter β and the estimator ĝ of the function g, to thereby acquire an estimator Ĝ of the function G.

108 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/348,455Filed 9 Nov 2017
Priority: KR 10-2016-01491209 Nov 2016
Published: 17 Oct 2019
Term ends: Not estimatedThe source records don’t support an estimate.

Classification10 codes

G06N 20/20
Machine learning: ensemble learning
G06F 17/18
Complex mathematical operations: for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression…
G06F 18/23
Analysing: clustering techniques
G06F 18/24323
Relating to the number of classes: tree-organised classifiers

Family

US 11,461,704 B2Related patent
US 2019/0318266 A1This patent

From the US filing history. Foreign family members aren’t listed.

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