A machine learning classifier system includes a data set processing subsystem to generate a training set and a validation set from multiple data sources. Classifier hardware induces a classifier according to the training set, and tests the classifier according to the validation set. A buffer connected to the classifier hardware stores data objects to be classified, and a register connected to the classifier hardware stores outputs of the classifier, including classified data objects.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-20. (canceled) 21. A machine learning classifier based procurement system comprising: at least one memory to store machine readable instructions; a plurality of machine learning classifiers, where one or more of the plurality of machine learning classifiers comprises: an ensemble classifier comprising a combination, the combination comprising: a machine learning logistic regression function, and at least one of: a decision tree function, a multicollinearity function, and a predictive strength analysis function, where: at least one of the decision tree function, the multicollinearity function, and the predictive strength analysis function are used to determine predictive variables, and the predictive variables are used in a training set and a validation set to generate the ensemble classifier according to the machine learning logistic regression function; and at least one processor, connected to the memory, to execute the machine readable instructions to: generate a solicitation comprised of a procurement request for an item or service; receive bids to provide the item or service; determine a price risk score, a supplier risk score, and an item risk score for each of the bids, where at least one of the price risk score, the supplier risk score, and the item risk score are determined based on classifications performed by the plurality of machine learning classifiers; compare, for each bid, the price risk score, the supplier risk score, and the item risk score to a respective threshold to determine if any of the received bids are associated with a high-risk procurement; and display, on a graphical user interface, the price risk score, the supplier risk score, and the item risk score for a received bid determined to be associated with the high-risk procurement. 22. The machine learning classifier based procurement system of claim 21, wherein the plurality of machine learning classifiers classify the bids as including at least one of a high-risk item, a high-risk supplier, and a high-risk price. 23. The machine learning classifier based procurement system of claim 21, comprising: a data set processing subsystem to generate the training set and the validation set from historic procurement data and the predictive variables, wherein the historic procurement data is comprised of historic bids to supply goods or services and associated procurement data received from a plurality of data sources. 24. The machine learning classifier based procurement system of claim 23, wherein to generate the training set and the validation set, the data set processing subsystem is to: store the historic bids received from a first data source; receive the associated procurement data from at least one other data source; store the associated procurement data with the historic bids; and partition the historic bids and the associated procurement data into first data for the training set and second date for the validation set, wherein the training set comprises a supervised training set of data objects and labels indicating whether each data object belongs to a particular category. 25. The machine learning classifier based procurement system of claim 24, wherein to receive the associated procurement data from at least one other data source, the data set processing subsystem is to: generate a query based on data in the historic bids received from the first data source; and execute the query on the at least one other data source to retrieve the associated procurement data from the at least one other data source. 26. The machine learning classifier based procurement system of claim 24, wherein the data set processing subsystem is to filter the stored historic bids and associated procurement data according to data scarcity and variation prior to partitioning the stored historic bids and the associated procurement data into the training set and the validation set. 27. The machine learning classifier based procurement system of claim 24, wherein the data set processing subsystem is to execute transformation operations on fields in the stored historic bids and associated procurement data prior to partitioning the historic bids and the associated procurement data into the training set and the validation set. 28. A procurement system comprising: at least one memory to store machine readable instructions; and at least one processor, connected to the memory, to execute the machine readable instructions to: generate a solicitation comprised of a procurement request for an item or service; receive bids to provide the item or service; evaluate the bids based on classifications performed by at least one machine learning classifier, wherein the at least one machine learning classifier classifies the bids as being associated with a high-risk supplier or not, being associated with a high-risk price or not, or being associated with a high-risk item or service or not; send an alert to a mobile device in response to identifying a high-risk bid based on the evaluation of the bids, the alert used to trigger an audit of the high-risk bid; receive a message from the mobile device, wherein the message indicates to accept the high-risk bid or to reject the high-risk bid; and identify a winning bid from the received bids based on the evaluation of the bids and the received message from the mobile device. 29. The procurement system of claim 28, wherein the at least one machine learning classifier comprises: an ensemble classifier comprising a combination, the combination comprising: a machine learning logistic regression function, and at least one of: a decision tree function, a multicollinearity function, and a predictive strength analysis function, where: at least one of the decision tree function, the multicollinearity function, and the predictive strength analysis function are used to determine predictive variables, and the predictive variables are used in a training set and a validation set to generate the ensemble classifier according to the machine learning logistic regression function. 30. The procurement system of claim 28, wherein prior to posting the solicitation, determining whether the solicitation specifies a high-risk item or service for procurement based on a classification of the solicitation performed by the at least one machine learning classifier. 31. The procurement system of claim 30, wherein in response to determining the solicitation includes the procurement request for a high-risk item or service, the at least one processor is to send a high-risk item or service alert to the mobile device to trigger a review of the solicitation before allowing the solicitation to be posted to receive the bids. 32. The procurement system of claim 30, wherein a training set is generated based on historic solicitation data for counterfeit or non-conforming goods or services, and the at least one machine learning classifier is generated based on the training set to classify solicitations as specifying a high-risk item or service or not. 33. The procurement system of claim 28, wherein to evaluate the bids, the at least one processor is to determine whether the bids specify a high-risk price based on a price estimation for the good or service specified in the procurement request, where the price estimation is determined from historic price data for procurements. 34. The procurement system of claim 33, wherein in response to determining a received bid specifies a high-risk price, the at least one processor is to send the alert to the mobile device, wherein the alert indicates that the received bid specifies a high-risk price. 35. The procurement system of claim 28, wherein to evaluate the bids, the at least one processor is to determine whether the bids specify a high-risk supplier based on classifications of the bids performed by the at least one machine learning classifier, wherein the classifications estimate whether the bids specify a high-risk supplier or not. 36. The procurement system of claim 35, wherein in response to determining a received bid specifies a high-risk supplier, the at least one processor is to send the alert to the mobile device, wherein the alert indicates that the received bid specifies a high-risk supplier. 37. A non-transitory computer readable medium storing machine readable instructions for a mobile application, the machine readable instructions executable by a processor of a mobile device to: receive an alert message from a procurement system executing an automated procurement process to solicit bids, evaluate the bids based on classifications performed by at least one machine learning classifier, and identify a winning bid for a procurement, wherein the alert message identifies a high-risk bid based on the evaluation of the bids and information describing the high-risk bid; trigger an audit process of the high-risk bid, the audit process producing an audit result; determine based on the audit result whether the automated procurement process is to accept the high-risk bid or reject the high-risk bid, the determining generating an identifier to accept the high-risk bid or an identifier to reject the high-risk bid; and send, from the mobile device, a message to the procurement system, the message including the identifier to accept the high-risk bid or the identifier to reject the high-risk bid, wherein the automated procurement process identifies the winning bid based on the message sent from the mobile device. 38. The non-transitory computer readable medium of claim 37, wherein the alert message identifying the high-risk bid indicates the high-risk bid specifies at least one of a high-risk price and a high-risk supplier. 39. The non-transitory computer readable medium of claim 38, wherein the audit process determines the audit result of whether the automated procurement process is to accept the high-risk bid or reject the high-risk bid based on the alert message specifying a received bid includes at least one of the high-risk price and the high-risk supplier. 40. The non-transitory computer readable medium of claim 37, wherein the at least one machine learning classifier comprises: an ensemble classifier comprising a combination, the combination comprising: a machine learning logistic regression function, and at least one of: a decision tree function, a multicollinearity function, and a predictive strength analysis function, where: at least one of the decision tree function, the multicollinearity function, and the predictive strength analysis function are used to determine predictive variables, and the predictive variables are used in a training set and a validation set to generate the ensemble classifier according to the machine learning logistic regression function.