Methods and systems for optimized selection of data features for a neuro-linguistic cognitive artificial intelligence system
AssigneeIntellective Ai, Inc.
InventorsGang Xu, Tao Yang, Ming-Jung Seow
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Abstract
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.
Claims18
A computer-implemented method to optimize feature selection to generate an adaptive linguistic model for a cognitive Artificial Intelligence (AI) system, the method comprising:
- receiving from a sensor, a sample vector of input data, the sample vector of input data including an indication of a sensor type associated with the sensor, the sensor type being one of an image sensor, a video sensor, an audio sensor, or a supervisory control and data acquisition (SCADA) sensor;
- generating a plurality of feature symbols by organizing the sample vector of the input data into probabilistic clusters;
- determining optimization parameters to optimize feature selection, the determining the optimization parameters based on the sensor type, the optimization parameters including at least one of a beta generation strategy parameter, a maturity thresholding parameter, or a maximal beta length;
- generating a plurality of betas based at least in part on the optimization parameters;
- and
- generating an adaptive linguistic model based at least in part on the plurality of betas, the adaptive linguistic model configured for a cognitive artificial intelligence system to distinguish between normal and abnormal activity in the input data.
A non-transitory computer-readable storage medium storing instructions, that when executed by a computer system, perform operations to generate an adaptive linguistic model, the instructions comprising instructions to:
- identify a sensor type associated with a sensor, based on an indication of the sensor type included in a sample vector of input data received from the sensor, the sensor type being one of an image sensor, a video sensor, an audio sensor, or a SCADA sensor;
- generate a plurality of feature symbols by organizing the sample vector of the input data into probabilistic clusters;
- determine, after identifying the sensor type, optimization parameters to optimize feature selection, the determination of the optimization parameters based on the sensor type, the optimization parameters including at least one of a beta generation strategy parameter, a maturity thresholding parameter, or a maximal beta length;
- generate a plurality of betas based at least in part on the optimization parameters;
- and
- generate the adaptive linguistic model based at least in part on the plurality of betas, the adaptive linguistic model configured for use in a cognitive artificial intelligence system to distinguish between normal and abnormal activity in the input data.
A system, comprising:
- a processor;
- and a memory, including an application program configured to perform operation for processing data, the operations comprising:
- identifying a sensor type associated with a sensor, based on an indication of the sensor type included in a sample vector of input data received from the sensor, the sensor type being one of an image sensor, a video sensor, an audio sensor, or a SCADA sensor;
- generating a plurality of feature symbols by organizing the sample vector of the input data into probabilistic clusters;
- determining, after identifying the sensor type, optimization parameters to optimize feature selection, the determination of the optimization parameters based on the sensor type, the optimization parameters including at least one of a beta generation strategy parameter, a maturity thresholding parameter, or a maximal beta length;
- generating a plurality of betas based at least in part on the optimization parameters;
- generating the adaptive linguistic model based at least in part on the plurality of betas, the adaptive linguistic model configured for distinguishing between normal and abnormal activity in the input data.
Dependencies are inferred from the claim text.