SPECTRAL CLASSIFICATION SYSTEMS AND METHODS
Various techniques are provided for training a neural network to classify chemical spectra data, such as Ion Mobility Spectrometry data. Machine learning models are trained using a training dataset comprising labeled chemical spectra data. The training process tracks chemical classification-related metrics and other informative metrics as the training dataset is processed. The trained models are tested using a validation dataset of chemical spectra data to generate performance results. A model analysis engine extracts and analyzes the informative metrics and performance results, generates parameters for a modified training dataset and features to improve model performance, and generates corresponding instructions to generate a new training dataset. The process repeats in an iterative fashion to build a final training dataset and set of models of classifying one or more chemicals.
1 . A system comprising:
a storage device configured to store a training dataset of labeled chemical spectra data, chemical classification models, performance criteria and results; and
a logic device configured to optimize the training dataset and chemical classification models for chemical classification through a process comprising:
determining at least one subset of the labeled chemical spectra data for training one or more of the chemical classification models;
determining at least one feature set for extracting features from the labeled chemical spectra data;
training a plurality of chemical classification models to classify one or more chemicals using the subset of labeled chemical spectra data and the feature set, and generating associated training metrics;
validating the trained chemical classification models and generate performance results; and
analyzing each trained chemical classification model using the associated training metrics and performance results and updating the subset of labeled chemical data, the feature set, and/or the training chemical classification models to optimize performance.
2 . The system of claim 1 , wherein the system is further configured to generate informative metrics representing a contribution of sample from the chemical spectra data to the trained chemical classification models.
3 . The system of claim 2 , wherein the logic device is further configured to execute a training dataset analysis engine configured to generate new chemical spectra data training dataset in response to the informative metrics.
4 . The system of claim 1 , wherein the logic device is further configured to execute a model validation system comprising a validation dataset comprising a plurality of labeled, chemical sample data, wherein the trained chemical classification model classifies chemicals from the validation dataset.
5 . The system of claim 4 , wherein the logic device is further configured to collect, and store labeled chemical spectra data.
6 . The system of claim 1 , wherein the logic device further comprises a feature analyzer configured to receive informative metrics and evaluate features of the chemical spectra data based on a contribution to the chemical classification model.
7 . The system of claim 1 , wherein the logic device further comprises a dataset generator configured to define an updated training dataset comprising a subset of the training dataset and parameters for a chemical sample data to be generated.
8 . The system of claim 7 , wherein the logic device further comprises an assembler/interface configured to process model parameters and generate instructions to control the logic device to generate chemical classification models in accordance with the parameters.
9 . The system of claim 8 , wherein the model parameters define a scope of the chemical classification models including at least one chemical to classify.
10 . The system of claim 1 , wherein the logic device is further configured to rank each sample of the chemical spectra data on a relative contribution to a performance of the chemical classification model.
11 . A method comprising:
storing a training dataset of labeled chemical spectra data, chemical classification models, performance criteria and results;
optimizing the training dataset and chemical classification models for chemical classification through processes comprising:
determining at least one subset of the labeled chemical spectra data for training one or more of the chemical classification models;
determining at least one feature set for extracting features from the labeled chemical spectra data;
training a plurality of chemical classification models to classify one or more chemicals using the subset of labeled chemical spectra data and the feature set, and generating associated training metrics;
validating the trained chemical classification models and generate performance results; and
analyzing each generated chemical classification model using the associated training metrics and performance results and updating the subset of labeled chemical data, the feature set, and/or the trained chemical classification models to optimize performance.
12 . The method of claim 11 , further comprising generating informative metrics representing a contribution of each sample from the chemical spectra data to the trained chemical classification models.
13 . The method of claim 12 , further comprising executing a training dataset analysis engine configured to generate new chemical spectra data training dataset in response to the informative metrics.
14 . The method of claim 11 , further comprising executing a chemical classification model validation system comprising a validation dataset comprising a plurality of labeled, chemical sample data, wherein the trained chemical classification model classifies chemicals from the validation dataset.
15 . The method of claim 14 , further comprising collecting and storing labeled chemical spectra data.
16 . The method of claim 11 , further comprising analyzing features by receiving informative metrics and evaluating features of the chemical spectra data based on a contribution to the chemical classification model.
17 . The method of claim 11 , further comprising generating a dataset by defining an updated training dataset comprising a subset of the training dataset and parameters for a chemical sample data to be generated.
18 . The method of claim 17 , further comprising executing an assembler/interface configured to process model parameters and generating chemical classification models in accordance with the parameters.
19 . The method of claim 18 , further comprising defining a scope of the chemical classification models including at least one chemical to classify.
20 . The method of claim 11 , further comprising ranking each of the plurality of chemical spectra data on a relative contribution to a performance of the chemical classification model