# US 12,602,596 B2 · Apparatus and method for validating dataset based on feature coverage > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,602,596 B2 - Document ID: US12602596B2 - Status: Granted - Kind: B2 - Application number: 17/455,325 - Filed: 2021-11-17 - Published: 2022-06-30 - Granted: 2026-04-14 - Earliest claimed: 2020-12-24 - Assignees: Ajou University Industry-Academic Cooperation Foundation - Applicants: Ajou University Industry-Academic Cooperation Foundation - Inventors: Jung Won Lee; Ye Seul Park; Chang Nam Lim - Web page: https://patentgrep.com/patent/US12602596B2 ## CPC - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06N 20/00: Machine learning - G06F 16/285: {Clustering or classification} - G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting - G06V 10/776: Validation; Performance evaluation - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## 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. ## Claims ### Claim 1 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. ### Claim 2 (depends on 1) The dataset validating method according to claim 1, wherein the second dataset is an arbitrary dataset having a plurality of data which is the same type as the first dataset and the classification model is an arbitrary learning model. ### Claim 3 (depends on 1) The dataset validating method according to claim 1, wherein in the clustering, a clustering algorithm or the number of clusterings is independently set for each label. ### Claim 4 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. ### Claim 5 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. ### Claim 6 (depends on 5) The dataset validating apparatus according to claim 5, wherein the second dataset is an arbitrary dataset having a plurality of data which is the same type as the first dataset and the classification model is an arbitrary learning model. ### Claim 7 (depends on 5) The dataset validating apparatus according to claim 5, wherein the hardware processor is further configured to independently set a clustering algorithm or the number of clusterings for each label. ### Claim 8 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.