GENERATION OF A CLASSIFIER FROM EXISTING CLASSIFIERS

• US 20190325261A1
• Filed: 04/10/2019
• Published: 10/24/2019
• Est. Priority Date: 04/19/2018
• Status: Abandoned Application

• Current Assignee: Fujitsu Limited
• Original Assignee: Fujitsu Limited
• Inventors: ODA, MITSURU
• Application Number:
  US16/380,454
• Publication Number:
  US 20190325261A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 18/217 Validation; Performance evaluation; Active pattern learning techniques
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06F 18/254 of classification results, e.g. of results related to same input data
• CPC Class Codes: G06F 18/285 Selection of pattern recognition techniques, e.g. of classifiers in a multi-classifier system
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/20 Ensemble learning

Abstract

An apparatus includes a memory that stores first and second classifier groups that have been learned using a subject that is acquired from an input domain including a plurality of subjects that are to be classified. The apparatus acquires, when correct answer data is input, a vector including evaluation values that are output by the first and second classifier groups as components, and selects a specific classifier group from among the first and second classifier groups, based on a dispersion relationship of the acquired vectors.

Claims

1. A classifier selection method executed by a processor included in an information processing device, the classifier selection method comprising:

providing first and second classifier groups that have been learned using a subject that is acquired from an input domain including a plurality of subjects that are to be classified;

acquiring, when correct answer data is input, a vector including evaluation values that are output by the first and second classifier groups as components; and

selecting a specific classifier group from among the first and second classifier groups, based on a dispersion relationship of the acquired vectors.

2. The classifier selection method according to claim 1,

wherein, in the selecting, a classifier group in which a dispersion of distances between feature points in a same classifier point set that is a set of feature points which have a same classification label in a feature space in which the vectors are arranged as feature points is selected as the specific classifier group.

3. The classifier selection method according to claim 2,

wherein, in the selecting, the specific classifier group is selected from a classifier group in which a dispersion of distances between feature points in the same classification point set is less than an average value.

4. The classifier selection method according to claim 2,

wherein, in the selecting, it is assumed that centers of gravity of the feature points in a plurality of the classification point sets are representative points, a minimum distance of distances between the representative points is calculated, and the specific classifier group is selected from a classifier group in which the minimum distance is an average value or more.

5. The classifier selection method according to claim 1, further comprising

classifying an input data group using the specific classifier group that has been selected.

6. The classifier selection method according to claim 1,

wherein, in the selecting, it is assumed that centers of gravity of the feature points included in a same classification point set that is a set of feature points that have a same classification label in a feature space in which the vectors are arranged as feature points are representative points, a minimum distance of distances between the representative points is calculated, it is determined, when classifiers that correspond to the feature points are removed from the first and second classifier groups, whether or not the minimum distance is increased, when the minimum distance is increased, a classifier group from which the classifiers that correspond to the feature points have been removed is selected as the specific classifier group, and, when the minimum distance is not increased, a classifier group from which the classifiers that correspond to the feature points have not been removed is selected as the specific classifier group.

7. The classifier selection method according to claim 6,

wherein, in the acquiring, based on peripheral information that characterizes a designated route, a feature vector group that corresponds to the peripheral information in accordance with a single route set that includes a start point node of the designated route is acquired,

each center-of-gravity vector of a first feature vector set that is a set of feature points for each same single route in a feature space in which the acquired feature vector group is arranged as a feature point is calculated, a distance between the centers-of-gravity vectors of all of single routes included in the single route is calculated, and a second feature vector set after removing a feature vector in which a distance from the center-of-gravity vector of the first feature vector set to which the single route itself does not belong is smaller than the distance between the centers of gravity vectors from the feature vector group is selected among the feature vectors included in the feature vector group, and

a current single route is determined based on the specific classifier group that has been selected and the second feature vector that has been selected.

8. A non-transitory, computer-readable recording medium having stored therein a program for causing a computer to execute a process comprising:

providing first and second classifier groups that have been learned using a subject that is acquired from an input domain including a plurality of subjects that are to be classified;

acquiring, when correct answer data is input, a vector including evaluation values that are output by the first and second classifier groups as components; and

selecting a specific classifier group from among the first and second classifier groups, based on a dispersion relationship of the acquired vectors.

9. An information processing apparatus comprising:

a memory configured to store first and second classifier groups that have been learned using a subject that is acquired from an input domain including a plurality of subjects that are to be classified; and

a processor coupled to the memory and configured to;

acquire, when correct answer data is input, a vector including evaluation values that are output by the first and second classifier groups as components, and

select a specific classifier group from among the first and second classifier groups, based on a dispersion relationship of the acquired vectors.

10. An information processing apparatus comprising:

a memory storing instructions; and

a processor, coupled to the memory, that executes the instructions to perform a process comprising;

establishing that centers of gravity of feature points are representative points when the feature points are included in a classification point set having a same classification label in a feature space in which vectors are arranged as the feature points;

determining distances among the representative points;

determining a minimum distance from among the distances;

determining whether the minimum distance increases when classifiers corresponding to the feature points are removed from a first classifier group and a second classifier group;

selecting a first specific classifier group from which the classifiers that correspond to the feature points have been removed when a determination is made that the minimum distance increases; and

selecting a second specific classifier group from which the classifiers that correspond to the feature points have not been removed when a determination is made that the minimum distance has not increased.