Methods and systems for optimized selection of data features for a neuro-linguistic cognitive artificial intelligence system
Techniques are disclosed to optimize feature selection in generating betas for a feature dictionary of a neuro-linguistic Cognitive AI System. A machine learning engine receives a sample vector of input data to be analyzed by the neuro-linguistic Cognitive AI System. The neuro-linguistic Cognitive AI System is configured to generate multiple betas for each of a plurality of sensors. The machine learning engine identifies a sensor specified in the sample vector and selects optimization parameters for generating betas based on the identified sensor.
1. A method, comprising:
receiving, via a processor and from a sensor, a first vector of input data, the first vector of input data indicating a type of the sensor;
identifying, via the processor, the sensor based on the first vector of input data;
generating, via the processor and based on a feature-combination rule set, a plurality of feature symbols by organizing the first vector of input data into probabilistic clusters;
generating, via the processor, an adaptive linguistic model based at least in part on a statistical distribution of the plurality of feature symbols and the type of the sensor;
repeatedly updating the adaptive linguistic model based on subsequent received vectors of input data; and
determining, based on the type of the sensor, a plurality of optimization parameters to optimize feature selection.
2. The method of claim 1 , wherein the sensor is a first sensor, the method further comprising receiving at least one vector of input data from the subsequent received vectors of input data from a second sensor different from the first sensor.
3. The method of claim 1 , further comprising receiving at least one vector of input data from the subsequent received vectors from an edge device different from the sensor.
4. The method of claim 1 , wherein the optimization parameters include at least one of a beta generation strategy parameter, a maturity thresholding parameter, or a maximal beta length.
5. The method of claim 1 , wherein the generating the adaptive linguistic model is further based on the plurality of optimization parameters.
6. The method of claim 1 , further comprising:
detecting an abnormal pattern in the received first vector input data based on the adaptive linguistic model; and
generating a notification in response to detecting the abnormal pattern.
7. The method of claim 1 , wherein generating the adaptive linguistic model further includes generating the adaptive linguist model based at least in part in the type of the sensor without using predefined activities found in the input data.
8. A non-transitory computer-readable storage medium storing instructions to cause a processor to:
receive a first vector of input data from a sensor, the first vector of input data indicating a type of the sensor, the type of the sensor being one of an image sensor, a video sensor, an audio sensor, or a supervisory control and data acquisition (SCADA) sensor;
organizing the first vector of input data into probabilistic clusters, based on a feature-combination rule set, to generate a plurality of feature symbols;
generate an adaptive linguistic model based at least in part on a statistical distribution of the plurality of feature symbols and the type of the sensor;
repeatedly update the adaptive linguistic model based on subsequent received vectors of input data; and
determine, based on the type of the sensor, a plurality of optimization parameters to optimize feature selection.
9. The non-transitory computer-readable storage medium of claim 8 , wherein the sensor is a first sensor, the non-transitory computer-readable storage medium further storing instructions to cause the processor to receive at least one vector of input data from the subsequent received vectors of input data from a second sensor different from the first sensor.
10. The non-transitory computer-readable storage medium of claim 8 , further storing instructions to cause the processor to receive at least one vector of input data from the subsequent received vectors of input data from an edge device different from the sensor.
11. The non-transitory computer-readable storage medium of claim 8 , wherein the optimization parameters include at least one of a beta generation strategy parameter, a maturity thresholding parameter, or a maximal beta length.
12. The non-transitory computer-readable storage medium of claim 8 , further storing instructions to cause a processor to generate the adaptive linguistic model based further on the plurality of optimization parameters.
13. The non-transitory computer-readable storage medium of claim 8 , further storing instructions to cause a processor to:
detect abnormal activity in the input data based on the adaptive linguistic model; and
generate a notification in response to detecting the abnormal activity.
14. The non-transitory computer-readable storage medium of claim 8 , further storing instructions to cause a processor to tune a plurality of parameters to produce a tuned plurality of parameters based on the sensor type, the instructions to cause the processor to generate the adaptive linguistic model including instructions to generate the adaptive linguistic model based further on the tuned plurality of parameters.
15. The non-transitory computer-readable storage medium of claim 8 , wherein the instructions to cause a processor to generate the adaptive linguistic model further includes instructions to cause the processor to generate the adaptive linguistic model based at least in part in the type of the sensor without using predefined activities found in the input data.
16. A system, comprising:
a processor; and
a memory storing an application configured to perform operations for processing data, the operations comprising:
receiving, via a processor and from a sensor, a first vector of input data, the first vector of input data indicating a type of the sensor;
generating, via the processor and based on a feature-combination rule set, a plurality of feature symbols by organizing the first vector of input data into probabilistic clusters;
generating, via the processor, an adaptive linguistic model based at least in part on a statistical distribution of the plurality of feature symbols and the type of the sensor;
repeatedly updating the adaptive linguistic model based on subsequent received vectors of input data; and
determining, based on the type of the sensor, a plurality of optimization parameters to optimize feature selection.
17. The system of claim 16 , wherein the type of the sensor is one of an image sensor, a video sensor, an audio sensor, or a supervisory control and data acquisition (SCADA) sensor.
18. The system of claim 16 , wherein the sensor is a first sensor, the operations further comprising receiving at least one vector of input data from the subsequent received vectors of input data from a second sensor different from the first sensor.
19. The system of claim 16 , wherein the operations further comprise receiving at least one vector of input data from the subsequent received vectors of input data from an edge device different from the sensor.
20. The system of claim 16 , wherein the optimization parameters include at least one of a beta generation strategy parameter, a maturity thresholding parameter, or a maximal beta length.
21. The system of claim 16 , wherein the instructions to cause the processor to generate the adaptive linguistic model including instructions to generate the adaptive linguistic model based further on the plurality of optimization parameters.
22. The system of claim 16 , wherein the operations further include:
detecting abnormal activity in the input data based on the adaptive linguistic model; and
generating a notification in response to detecting the abnormal activity.
23. The system of claim 16 , wherein generating the adaptive linguistic model further includes generating the adaptive linguist model based at least in part in the type of the sensor without using predefined activities found in the input data.