METHODS AND SYSTEMS FOR OPTIMIZED SELECTION OF DATA FEATURES FOR A NEURO-LINGUISTIC COGNITIVE ARTIFICAL 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 feature words 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 feature words based on the identified sensor.
1 . A computer-implemented method to optimize feature selection to generate an adaptive linguistic model, the method comprising:
receiving from a sensor, a sample vector of input data, the sample vector of input data indicating a type of the sensor;
identifying via a processor, the sensor based on the sample vector of input data;
generating via the processor, a plurality of feature symbols by organizing the sample vector of input data into probabilistic clusters;
determining via the processor, optimization parameters based on the sensor identified;
generating via the processor, a plurality of feature words based at least in part on at least one of combinations of the plurality of feature symbols or combinations of the optimization parameters; and
generating via the processor, the adaptive linguistic model based at least in part on combinations of the plurality of feature words.
2 . The computer-implemented method of claim 1 , wherein the sample vector of input data is a first sample vector of input data, the method further comprising: receiving from the sensor, a second sample vector of input data;
generating via the processor, a plurality of feature words for the second sample vector of input data; and
updating via the processor, the adaptive linguistic model based at least in part on the plurality of feature words generated for the second sample vector of input data.
3 . The computer-implemented method of claim 1 , wherein determining the optimization parameters further comprises:
tuning a plurality of parameters based on the type of the sensor, the adaptive linguistic model being generated at least in part on the plurality of parameters.
4 . The computer-implemented method of claim 3 , wherein:
the tuning is based on at least one of a maximum length of each feature word in the plurality of feature words, a maturity threshold for each feature word in the plurality of feature words, a statistical significance of each feature word in the plurality of feature words, or a feature-combination rule set,
the feature-combination rule set is generated from the plurality of feature words.
5 . The computer-implemented method of claim 1 , wherein the sensor is at least one of an image sensor, a video sensor, an audio sensor, or a SCADA sensor.
6 . The computer-implemented method of claim 1 , wherein the optimization parameters differ based on the type of the sensor.
7 . The computer-implemented method of claim 1 , wherein generating the adaptive linguistic model includes determining a tunable strategy to generate the plurality of feature words based on the type of the sensor.
8 . A non-transitory computer-readable storage medium storing instructions, which when executed by a computer system, perform operations for generating an adaptive linguistic model, the operations comprising:
identifying a sensor based on a sample vector of input data, the sample vector of input data obtained from the sensor and indicating a type of the at least one sensor;
generating a plurality of feature symbols by organizing the sample vector of input data into probabilistic clusters;
determining, after the identifying, optimization parameters based on the sensor;
generating a plurality of feature words based at least in part on at least one of combinations of the plurality of feature symbols or combinations of the optimization parameters; and
generating the adaptive linguistic model based at least in part on combinations of the plurality of feature words.
9 . The computer-readable storage medium of claim 8 , wherein the sample vector of input data is a first sample vector of input data, the operations further comprising:
identifying the sensor based on a second sample vector of input data;
generating, after identifying, a plurality of feature words for the second sample vector of input data; and
updating the adaptive linguistic model based at least in part on the plurality of feature words generated for the second sample vector of input data.
10 . The computer-readable storage medium of claim 8 , wherein determining the optimization parameters further comprises:
tuning a plurality of parameters based on the type of the sensor, the adaptive linguistic model being generated at least in part on the plurality of parameters.
11 . The computer-readable storage medium of claim 10 , wherein:
the tuning is based on at least one of a maximum length of each feature word in the plurality of feature words, a maturity threshold for each feature word in the plurality of feature words, a statistical significance of each feature word in the plurality of feature words, or a feature-combination rule set,
the feature-combination rule set is generated from the plurality of feature words.
12 . The computer-readable storage medium of claim 8 , wherein the sensor is at least one of an image sensor, a video sensor, an audio sensor, or a SCADA sensor.
13 . The computer-readable storage medium of claim 8 , wherein generating the adaptive linguistic model includes determining a tunable strategy to generate the plurality of feature words based on the type of the sensor.
14 . The computer-readable storage medium of claim 8 , wherein the optimization parameters differ based on the type of the sensor.
15 . 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 based on a sample vector of input data, the sample vector of input data obtained from the sensor and indicating a type of the at least one sensor;
generating a plurality of feature symbols by organizing the sample vector of input data into probabilistic clusters;
determining, after the identifying, optimization parameters based on the sensor;
generating a plurality of feature words based at least in part on at least one of combinations of the plurality of feature symbols or combinations of the optimization parameters; and
generating the adaptive linguistic model based at least in part on combinations of the plurality of feature words.
16 . The system of claim 15 , wherein the sample vector of input data is a first sample vector of input data, the operations further comprising:
identifying the sensor based on a second sample vector of input data;
generating, after identifying, a plurality of feature words for the second sample vector of input data; and
updating the adaptive linguistic model based at least in part on the plurality of feature words generated for the second sample vector of input data.
17 . The system of claim 15 , wherein determining the optimization parameters further comprises:
tuning a plurality of parameters based on the type of the sensor, the adaptive linguistic model being generated at least in part on the plurality of parameters.
18 . The system of claim 17 , wherein:
the tuning is based on at least one of a maximum length of each feature word in the plurality of feature words, a maturity threshold for each feature word in the plurality of feature words, a statistical significance of each feature word in the plurality of feature words, or a feature-combination rule set,
the feature-combination rule set is generated from the plurality of feature words.
19 . The system of claim 15 , wherein generating the adaptive linguistic model includes determining a tunable strategy to generate the plurality of feature words based on the type of the sensor.
20 . The system of claim 15 , wherein the optimization parameters differ based on the type of the sensor.