# US 2019/0171971 A1 · Information processing apparatus, program, and information processing method

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- Number: US 2019/0171971 A1
- Document ID: US20190171971A1
- Status: Published application
- Kind: A1
- Application number: 16/268,815
- Filed: 2019-02-06
- Published: 2019-06-06
- Earliest claimed: 2014-09-22
- Assignees: International Business Machines Corporation
- Applicants: International Business Machines Corporation
- Inventors: Takayuki Katsuki; Yuma Shinohara
- Web page: https://patentgrep.com/patent/US20190171971A1

## CPC

- G06N 20/00: Machine learning
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Various embodiments train a prediction model for predicting a label to be allocated to a prediction target explanatory variable set. In one embodiment, one or more sets of training data are acquired. Each of the one or more sets of training data includes at least one set of explanatory variables and a label allocated to the at least one explanatory variable set. A plurality of explanatory variable subsets is extracted from the at least one set of explanatory variables. A prediction model is trained utilizing the training data. The plurality of explanatory variable subsets is reflected on a label predicted by the prediction model to be allocated to a prediction target explanatory variable set with each of the plurality of explanatory variable subsets weighted respectively.

## Claims

### Claim 1

A method for training a prediction model for predicting a label to be allocated to a prediction target explanatory variable set, the method comprising:
acquiring a set of training data, the set of training data comprising a set of explanatory variables and a label allocated to the set of explanatory variables;
extracting a plurality of explanatory variable subsets from the set of explanatory variables; and
training a prediction model utilizing the set of training data, wherein the prediction model generates a predicted label to be allocated to a prediction target explanatory variable set with each of the plurality of explanatory variable subsets weighted respectively.

### Claim 2 (depends on 1)

The method according to claim 1, wherein training the prediction further comprises:
allocating a different weight coefficient to each of the plurality of explanatory variable subsets.

### Claim 3 (depends on 2)

The method according to claim 2, further comprising:
generating a feature vector, concerning each of the plurality of explanatory variable subsets, the feature vector comprising a plurality of feature values, wherein training the prediction model further comprises:
utilizing a regression vector comprising a plurality of regression coefficients respectively corresponding to the plurality of feature values of the feature vector and the weight coefficient of each of plurality of explanatory variable subsets.

### Claim 4 (depends on 3)

The method according to claim 3, wherein training the prediction model further comprises:
executing Bayesian inference using prior distributions of the regression vector, the weight coefficients, and the training data; and
outputting a posterior probability distribution of the regression vector and the weight coefficients as a training result.

### Claim 5 (depends on 4)

The method according to claim 4, wherein training the prediction model further comprises:
utilizing an objective function to be minimized for training the prediction model, the objective function comprising a weighted sum of terms indicating errors between labels predicted for the plurality of explanatory variable subsets based on the feature vector and the regression vector, and the label allocated to the set of explanatory variables.

### Claim 6 (depends on 4)

The method according to claim 4, wherein training the prediction model further comprises:
utilizing, as prior distributions, the posterior probability distribution of the regression vector and the weight coefficients; and
outputting posterior probability distributions of the regression vector and the weight coefficients for additional training data.

### Claim 7 (depends on 1)

The method according to claim 1, wherein the set of training data is a time-series data set observed over time, and wherein the extracting further comprises:
extracting, as the plurality of explanatory variable subsets, a plurality of data sequences continuous in time series.

### Claim 8 (depends on 7)

The method according to claim 7, wherein the plurality of data sequences comprises a set of values of a plurality of feature values in a plurality of sections.

### Claim 9 (depends on 7)

The method according to claim 7, wherein the plurality of data sequences partially overlap one another in a time series.

### Claim 10 (depends on 1)

The method according to claim 1, wherein the acquiring further comprises:
acquiring a prediction target data set comprising the prediction target explanatory variable set, and wherein method further comprises:
generating the predicted label corresponding to the prediction target explanatory variable set based on the prediction model.

### Claim 11 (depends on 10)

The method according to claim 10, wherein the training further comprises:
setting, as additional training data, the prediction target data set; and
further training the prediction model based on the prediction target data set.

### Claim 12

An information processing apparatus for training a prediction model for predicting a label to be allocated to a prediction target explanatory variable set, the information processing apparatus comprising:
a memory;
a processor communicatively coupled to the memory;
an acquiring unit to acquire a set of training data, the set of training data comprising a set of explanatory variables and a label allocated to the set of explanatory variables;
an extracting unit to extract a plurality of explanatory variable subsets from the set of explanatory variables; and
a training processing unit to train a prediction model, where the prediction model is trained utilizing the set of training data, wherein the prediction model generates a predicted label to be allocated to a prediction target explanatory variable set with each of the plurality of explanatory variable subsets weighted respectively.

### Claim 13 (depends on 12)

The information processing apparatus according to claim 12, wherein the acquiring unit is further configured to:
acquire a prediction target data set comprising the prediction target explanatory variable set, and wherein the information processing apparatus further comprises:
a predicting unit to generate the predicted label corresponding to the prediction target explanatory variable set based on the prediction model.

### Claim 14 (depends on 12)

The information processing apparatus according to claim 12, wherein the training processing unit trains the prediction model by allocating a different weight coefficient to each of the plurality of explanatory variable subsets.

### Claim 15 (depends on 14)

The information processing apparatus according to claim 14, further comprising a feature vector generating unit to generate a feature vector comprising a plurality of feature values, concerning each of the plurality of explanatory variable subsets; and
wherein the training processing unit trains the prediction model by utilizing a regression vector comprising a plurality of regression coefficients respectively corresponding to the plurality of feature values of the feature vector and the weight coefficient of each of plurality of explanatory variable subsets.

### Claim 16

A computer program product for training a prediction model for predicting a label to be allocated to a prediction target explanatory variable set, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:
acquiring a set of training data, the set of training data comprising a set of explanatory variables and a label allocated to the set of explanatory variables;
extracting a plurality of explanatory variable subsets from the set of explanatory variables; and
training a prediction model utilizing the set of training data, wherein the prediction model generates a predicted label to be allocated to a prediction target explanatory variable set with each of the plurality of explanatory variable subsets weighted respectively.

### Claim 17 (depends on 16)

The computer program product according to claim 16, where training the prediction further comprises:
allocating a different weight coefficient to each of the plurality of explanatory variable subsets.

### Claim 18 (depends on 17)

The computer program product according to claim 17, wherein the method further comprises:
generating a feature vector, concerning each of the plurality of explanatory variable subsets, comprising a plurality of feature values, wherein training the prediction model further comprises:
utilizing a regression vector comprising a plurality of regression coefficients respectively corresponding to the plurality of feature values of the feature vector and the weight coefficient of each of plurality of explanatory variable subsets.

### Claim 19 (depends on 18)

The computer program product according to claim 18, wherein training the prediction model further comprises:
executing Bayesian inference using prior distributions of the regression vector, the weight coefficients, and the training data; and
outputting a posterior probability distribution of the regression vector and the weight coefficients as a training result.

### Claim 20 (depends on 19)

The computer program product according to claim 19, wherein training the prediction model further comprises:
utilizing an objective function to be minimized for training the prediction model, the objective function comprising a weighted sum of terms indicating errors between labels predicted for the plurality of explanatory variable subsets based on the feature vector and the regression vector, and the label allocated to the set of explanatory variables.