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US 12,602,596 B2Granted
Apparatus and method for validating dataset based on feature coverage
AssigneeAjou University Industry-Academic Cooperation Foundation
InventorsJung Won Lee, Ye Seul Park, Chang Nam Lim
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Abstract
A dataset validating method based on a feature coverage according to an exemplary embodiment of the present disclosure includes extracting a feature of a first dataset including a plurality of data using a classification model trained for a predetermined second dataset; clustering labels of the first dataset according to the extracted feature; and validating a coverage of a partial dataset which is a part selected from the first dataset based on the clustering result.
Claims8
A dataset validating method based on a feature coverage and performed by a dataset validating apparatus comprising a hardware processor and a memory storing instructions executable by the hardware processor, the method comprising:
- providing a first dataset to a classification model to extract a plurality of features of data included in the first dataset, the classification model being stored in the memory and trained, using a predetermined second dataset, to extract a feature of input data;
- providing the extracted plurality of features to a predetermined dimension reduction algorithm stored in the memory to reduce a dimension of the plurality of features to less than or equal to a predetermined dimension;
- clustering the dimension-reduced plurality of features into a plurality of clusters, wherein each label of a plurality of labels includes at least two clusters from among the plurality of clusters, the plurality of labels being classification results for input data of the classification model;
- validating a coverage of a partial dataset which is a part selected from the first dataset based on the clustering result, by confirming a cluster of a label in which individual data included in the partial dataset is included to validate how much the partial dataset covers a feature coverage corresponding to a label and a cluster;
- and
- storing the coverage in the memory,
- wherein the coverage is defined as a ratio of the number of clusters included in the partial dataset and a total number of clusters.
4 more elements
A dataset generating method based on a feature coverage and performed by a dataset generating apparatus comprising a hardware processor and a memory storing instructions executable by the hardware processor, the method comprising:
- providing a first dataset to a classification model to extract a plurality of features of data included in the first dataset, the classification model being stored in the memory and trained, using a predetermined second dataset, to extract a feature of input data;
- providing the extracted plurality of features to a predetermined dimension reduction algorithm to reduce a dimension of the plurality of features to less than or equal to a predetermined dimension;
- clustering the dimension-reduced plurality of features into a plurality of clusters, wherein each label of a plurality of labels includes at least two clusters from among the plurality of clusters, the plurality of labels being classification results for input data of the classification model;
- and
- selecting a partial dataset which is a part of the first dataset, and storing the partial dataset in the memory,
- wherein the partial dataset is selected such that data included in the partial dataset is distributed in all the clusters and a distribution ratio for the data included in the partial dataset over the clusters corresponds to a distribution ratio of the first dataset within a predetermined range.
3 more elements
A dataset validating apparatus based on a feature coverage, the dataset validating apparatus comprising:
- a hardware processor;
- and
- a memory storing instructions executable by the hardware processor, a classification model, and a predetermined dimension reduction algorithm,
- wherein the hardware processor is configured to:
- provide a first dataset to the classification model to extract a plurality of features of data included in the first dataset, the classification model being trained, using a predetermined second dataset, to extract a feature of input data,
- provide the extracted plurality of features to the predetermined dimension reduction algorithm to reduce a dimension of the plurality of features to less than or equal to a predetermined dimension,
- cluster the dimension-reduced plurality of features into a plurality of clusters, wherein each label of a plurality of labels includes at least two clusters from among the plurality of clusters, the plurality of labels being classification results for input data of the classification model,
- validate a coverage of a partial dataset which is a part selected from the first dataset based on the clustering result, by confirming a cluster of a label in which individual data included in the partial dataset is included to validate how much the partial dataset covers a feature coverage corresponding to a label and a cluster, and
- store the coverage in the memory,
- wherein the coverage is defined as a ratio of the number of clusters included in the partial dataset and a total number of clusters.
7 more elements
A dataset generating apparatus based on a feature coverage, the apparatus comprising:
- a hardware processor;
- and
- a memory storing instructions executable by the hardware processor, and a classification model,
- wherein the hardware processor is configured to:
- provide a first dataset to the classification model to extract a plurality of features of data included in the first dataset, the classification model being trained, using a predetermined second dataset, to extract a feature of input data,
- provide the extracted plurality of features to a predetermined dimension reduction algorithm to reduce a dimension of the plurality of features to less than or equal to a predetermined dimension,
- cluster the dimension-reduced plurality of features into a plurality of clusters, wherein each label of a plurality of labels includes at least two clusters from among the plurality of clusters, the plurality of labels being classification results for input data of the classification model,
- select a partial dataset which is a part of the first dataset, and store the partial dataset in the memory,
- wherein the partial dataset is selected such that data included in the partial dataset is distributed in all the clusters and a distribution ratio for the data included in the partial dataset over the clusters corresponds to a distribution ratio of the first dataset within a predetermined range.
6 more elements
Dependencies are inferred from the claim text.