| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_53f347c8f604f15767e8e73fec62095a |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-084 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2113
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-23
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2433
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-22
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-217
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6218
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6262
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-623
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6215 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 |
| filingDate |
2020-10-29^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_9006cfc2d0f0923c5ce21945ebb118c3
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0c1fd007653487052056355d7e1084e4
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ccd25c901ef12ee85f8a44c00edbf777
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5bb38904c16defdc595cba7016ddfb1a
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_d0f56c5ec89009c31e789c4949c037b2
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4dbe1314e6dda8889089f87dff1e0b37
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4daf80954001a08bf8700cfca4d3ca00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5b8c2d20ffd8eeee1c1cd32994cffa69
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_3cfea79d23c895bd054efdd3a412bb1c |
| publicationDate |
2022-05-05^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2022138504-A1 |
| titleOfInvention |
Separation maximization technique for anomaly scores to compare anomaly detection models |
| abstract |
In an embodiment based on computer(s), an ML model is trained to detect outliers. The ML model calculates anomaly scores that include a respective anomaly score for each item in a validation dataset. The anomaly scores are automatically organized by sorting and/or clustering. Based on the organized anomaly scores, a separation is measured that indicates fitness of the ML model. In an embodiment, a computer performs two-clustering of anomaly scores into a first organization that consists of a first normal cluster of anomaly scores and a first anomaly cluster of anomaly scores. The computer performs three-clustering of the same anomaly scores into a second organization that consists of a second normal cluster of anomaly scores, a second anomaly cluster of anomaly scores, and a middle cluster of anomaly scores. A distribution difference between the first organization and the second organization is measured. An ML model is processed based on the distribution difference. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2023004857-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115510302-A |
| priorityDate |
2020-10-29^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |