Classifier with outlier detection algorithm

A method for a computing system comprising a processor and a non-volatile memory storing executable instructions, the method comprising:

• executing a classifier including an unsupervised artificial intelligence model and a supervised artificial intelligence model, the supervised artificial intelligence model being logically distinct from the unsupervised artificial intelligence model, the unsupervised artificial intelligence model comprising a first neural network and the supervised artificial intelligence model comprising a second neural network, executing the classifier including:
• receiving run-time input data;
• processing the run-time input data using the unsupervised artificial intelligence model and an outlier detection algorithm to determine whether the run-time input data is an outlier as compared to training input data;
• responsive to determining that the run-time input data is not an outlier, determining a predicted response label for the run-time input data based on the run-time input data processed using the supervised artificial intelligence model;
• and
• responsive to determining that the run-time input data is an outlier, refraining from determining the predicted response label for the run-time input data based on the run-time input data processed using the supervised artificial intelligence model, and instead outputting a prompt for user input of a user-curated response label for the run-time input data,
• wherein the unsupervised artificial intelligence model includes a clustering algorithm or a self-organizing map configured to identify a plurality of clusters in the training input data, classify the run-time input data as belonging to a classified cluster of the plurality of identified clusters, wherein the outlier detection algorithm is configured to determine whether the run-time input data is an outlier based upon a proximity of the run-time input data to the classified cluster;
• and
• wherein the clustering algorithm of the unsupervised artificial intelligence model implements one or more of hierarchical clustering, k-means clustering, mixture modeling, a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, and an OPTICS (Ordering Points To Identify the Clustering Structure) algorithm.

A computing system, comprising:

• a processor and a non-volatile memory storing executable instructions that, in response to execution by the processor, cause the processor to:
• execute a classifier including an unsupervised artificial intelligence model and a supervised artificial intelligence model, the supervised artificial intelligence model being logically distinct from the unsupervised artificial intelligence model, the unsupervised artificial intelligence model comprising a first neural network and the supervised artificial intelligence model comprising a second neural network, the classifier being configured to:
• receive run-time input data including an image;
• process the run-time input data using the unsupervised artificial intelligence model and an outlier detection algorithm to determine whether the run-time input data is an outlier as compared to training input data;
• responsive to determining that the run-time input data is not an outlier, determine a predicted response label for the run-time input data based on the run- time input data processed using the supervised artificial intelligence model, wherein the predicted response label is one vehicle type label from a defined set of vehicle type labels;
• and
• responsive to determining that the run-time input data is an outlier, refrain from determining the predicted response label for the run-time input data based on the run-time input data processed using the supervised artificial intelligence model, and instead output a prompt for user input of a user-curated response label for the run-time input data,
• wherein the unsupervised artificial intelligence model includes a clustering algorithm or a self-organizing map configured to identify a plurality of clusters in the training input data, classify the run-time input data as belonging to a classified cluster of the plurality of identified clusters, wherein the outlier detection algorithm is configured to determine whether the run-time input data is an outlier based upon a proximity of the run-time input data to the classified cluster;
• wherein the training input data includes a collection of images from a camera on board a vehicle, the images capturing an external environment of the vehicle;
• wherein ground truth labels associated with the training input data include a vehicle type label for each of the images selected from the defined set of vehicle type labels;
• and
• wherein the vehicle is an aircraft and the vehicle type labels are aircraft type labels.