Systems and methods are disclosed herein for tuning an item classification. In one aspect, a method may include receiving, by a computing device, a classification request. The computing device may determine an item type of the item using a plurality of classifiers, and generate information of item type determination corresponding to the item. In response to a determination that a confidence score associated with the determined item type is less than a predetermined threshold, the computing device may provide the information of item type determination for analysis.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for classification, the method comprising: receiving, by one or more processors of a server, a classification request comprising a description of an item; determining, by the one or more processors, an item type of the item using a plurality of classifiers; generating, by the one or more processors, information of item type determination corresponding to the item, the information of item type determination comprising the description of the item and a determined item type; determining, by the one or more processors, whether a confidence score associated with the determined item type is less than a predetermined threshold; and in response to a determination that the confidence score associated with the determined item type is less than the predetermined threshold, providing, by the one or more processors, the information of item type determination for analysis. 2. The method of claim 1, further comprising: in response to a determination that the confidence score associated with the determined item type is not less than the predetermined threshold, providing, by the one or more processors, the information of item type determination to a crowdsourcing computer network for evaluation. 3. The method of claim 2, further comprising: storing the information of item type determination corresponding to the item; and generating a report comprising the analysis or the evaluation. 4. The method of claim 2, further comprising: determining, based on the evaluation, that the determined item type for the item is not acceptable; and causing generation of a new rule and a updated item type determination. 5. The method of claim 4, further comprising: updating a rule-based classifier using the new rule; and training a machine learning-based classifier using the updated item type determination, the plurality of classifiers comprising the rule-based classifier and the machine learning-based classifier. 6. The method of claim 5, further comprising: generating a report of item type determination of the item based on the evaluation and the updated item type determination; and providing the report to a user. 7. The method of claim 1, wherein the description of the item comprises a suggested item type. 8. The method of claim 7, further comprising: determining that the determined item type comprises the suggested item type; and providing the information of the item type determination to a crowdsourcing computer network for evaluation. 9. The method of claim 1, wherein the plurality of classifiers comprises a plurality of rule-based classifiers that implement a white-list rule and a blacklist rule for item classification, wherein the white-list rule defines that the item belongs to a first item type, and wherein the blacklist rule defines that the item does not belong to a second item type different from the first item type. 10. The method of claim 1, wherein the plurality of classifiers comprises at least one of a naive Bayes classifier, a k-Nearest Neighbors (KNN) classifier, or a Perceptron classifier. 11. One or more computer-readable media storing computer-executable instructions that, when executed on one or more processors, cause the one or more processors to perform acts comprising: receiving a classification request comprising a description of an item; determining an item type of the item using a plurality of classifiers; generating information of item type determination corresponding to the item, the information of item type determination comprising the description of the item and a determined item type; determining that a confidence score associated with the determined item type is less than a predetermined threshold; and providing the information of item type determination for analysis by an analyst. 12. The one or more computer-readable media of claim 11, wherein the acts further comprise: determining that the confidence score associated with the determined item type is not less than the predetermined threshold; and providing the information of item type determination to a crowdsourcing computer network for evaluation. 13. The one or more computer-readable media of claim 12, wherein the acts further comprise: determining, based on the evaluation, that the determined item type for the item is not acceptable; causing generation of a new rule and a updated item type determination; updating a rule-based classifier using the new rule; and training a machine learning-based classifier using the updated item type determination, the plurality of classifiers comprising the rule-based classifier and the machine learning-based classifier. 14. The one or more computer-readable media of claim 11, wherein the plurality of classifiers comprises a plurality of rule-based classifiers that implement a white-list rule and a blacklist rule for item classification, wherein the white-list rule defines that the item belongs to a first item type, and wherein the blacklist rule defines that the item does not belong to a second item type different from the first item type. 15. The one or more computer-readable media of claim 11, wherein the description of the item comprises a suggested item type, and wherein the acts further comprise: determining that the determined item type comprises the suggested item type; and providing the information of the item type determination to a crowdsourcing computer network for evaluation. 16. An apparatus comprising: a memory configured to store data and one or more sets of instructions; and one or more processors coupled to the memory, the one or more processors configured to execute the one or more sets of instructions and perform operations comprising: receiving a classification request comprising a description of an item; determining an item type of the item using a plurality of classifiers; generating information of item type determination corresponding to the item, the information of item type determination comprising the description of the item and a determined item type; determining whether a confidence score associated with the determined item type is less than a predetermined threshold; and in response to a determination that the confidence score associated with the determined item type is less than the predetermined threshold, providing the information of item type determination for analysis. 17. The apparatus of claim 16, wherein the operations further comprise: in response to a determination that the confidence score associated with the determined item type is not less than the predetermined threshold, providing the information of item type determination to a crowdsourcing computer network for evaluation; storing the information of item type determination corresponding to the item; and generating a report comprising the analysis or the evaluation. 18. The apparatus of claim 17, wherein the operations further comprise: determining, based on the evaluation, that the determined item type for the item is not acceptable; causing generation of a new rule and a updated item type determination; updating a rule-based classifier using the new rule; and training a machine learning-based classifier using the updated item type determination, the plurality of classifiers comprising the rule-based classifier and the machine learning-based classifier. 19. The apparatus of claim 16, wherein the description of the item comprises a suggested item type. 20. The apparatus of claim 19, wherein the operations further comprise: determining that the determined item type comprises the suggested item type; and providing the information of the item type determination to a crowdsourcing computer network for evaluation.