Systems and methods are disclosed herein for rule-based item classification. The methods include receiving, by a computing device, an item record for analysis. The computing device may determine ranked lists of item types using rule-based classifiers and machine learning-based classifiers. Then, the computing device may aggregate the ranked lists of item types to generate a combined ranked list of item types.
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, an item record associated with an item, the item record comprising a description of the item; generating, by the one or more processors, a first ranked list of item types based on the item record using a plurality of rule-based classifiers; generating, by the one or more processors, a second ranked list of item types based on the item record using a plurality of machine learning-based classifiers; and aggregating, by the one or more processors, the first ranked list of item types and the second ranked list of item types to generate a combined ranked list of item types. 2. The method of claim 1, wherein the generating the first ranked list of item types based on the item record using the plurality of rule-based classifiers comprises: applying a whitelist rule and a blacklist rule to the item record to obtain the first ranked list of item types, the whitelist rule defining that the item belongs to a first item type, the blacklist rule defining that the item does not belong to a second item type different from the first item type. 3. The method of claim 1, further comprising: providing at least a portion of the combined ranked list of item types to a crowdsourcing computer network for evaluation, the at least a portion of the combined ranked list of item types comprising a determined item type of the item; determining, based on the evaluation, that the determined item type is not acceptable; and causing generation of a new rule and an updated item type by an analyst. 4. The method of claim 3, further comprising: updating at least one classifier of the plurality of rule-based classifiers using the new rule; and training at least one classifier of the plurality of machine learning-based classifiers using the updated item type and the description of the item. 5. The method of claim 1, further comprising: determining a third ranked list of item types based on the item record using a plurality of attribute-based classifiers. 6. The method of claim 5, wherein the aggregating the first ranked list of item types and the second ranked list of item types into the combined ranked list of item types comprises: aggregating the first ranked list of item types, the second ranked list of item types, and the third ranked list of item types into the combined ranked list of item types. 7. The method of claim 1, further comprising: initializing at least a portion of the plurality of rule-based classifiers and the plurality of machine learning-based classifiers using a set of training item records and a basic rule. 8. The method of claim 1, further comprising: analyzing the item record to determine an initial item type based on an initial classification rule; and providing the item record to the plurality of rule-based classifiers or the plurality of machine learning-based classifiers in response to a determination that: a confidence score associated with the initial item type and the item is greater than a predetermined threshold, or the initial classification rule is not related to the item record. 9. The method of claim 1, wherein the plurality of machine learning-based classifiers comprises at least one of a naive Bayes classifier, a k-Nearest Neighbors (KNN) classifier, or a Perceptron classifier. 10. The method of claim 1, further comprising: filtering the combined ranked list of item types to generate a filtered list using a blacklist rule; and providing a representation of the filtered list. 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 an item record associated with an item, the item record comprising a description of the item; classifying the item based on the item record using a plurality of rule-based classifiers to generate a first ranked list of item types; classifying the item based on the item record using a plurality of attribute-based classifiers to generate a second ranked list of item types; and combining the first ranked list of item types and the second ranked list of item types to generate a combined ranked list of item types. 12. The one or more computer-readable media of claim 11, wherein the classifying the item based on the item record using a plurality of rule-based classifiers to generate a first ranked list of item types comprises: applying a whitelist rule and a blacklist rule to the item record to obtain the first ranked list of item types, the whitelist rule defining that the item belongs to a first item type, the blacklist rule defining that the item does not belong to a second item type different from the first item type. 13. The one or more computer-readable media of claim 11, wherein the acts further comprise: providing at least a portion of the combined ranked list of item types to a crowdsourcing computer network for evaluation, the at least a portion of the combined ranked list of item types comprising a determined item type of the item; determining, based on the evaluation, that the determined item type is not acceptable; and causing generation of a new rule and an updated item type by an analyst. 14. The one or more computer-readable media of claim 13, wherein the acts further comprise: updating at least one classifier of the plurality of rule-based classifiers using the new rule; and training at least one classifier of the plurality of machine learning-based classifiers using the updated item type and the description of the item. 15. The one or more computer-readable media of claim 11, wherein the acts further comprise: determining a third ranked list of item types based on the item record using a plurality of machine learning-based classifiers, and wherein the combining the first ranked list of item types and the second ranked list of item types into the combined ranked list of item types comprises: aggregating the first ranked list of item types, the second ranked list of item types, and the third ranked list of item types into the combined ranked list of item types. 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 an item record associated with an item, the item record comprising a description of the item; generating a first ranked list of item types based on the item record using a plurality of rule-based classifiers; generating a second ranked list of item types based on the item record using a plurality of machine learning-based classifiers; and aggregating the first ranked list of item types and the second ranked list of item types to generate a combined ranked list of item types. 17. The apparatus of claim 16, wherein the generating the first ranked list of item types based on the item record using the plurality of rule-based classifiers comprises: applying a whitelist rule and a blacklist rule to the item record to obtain the first ranked list of item types, the whitelist rule defining that the item belongs to a first item type, the blacklist rule defining that the item does not belong to a second item type different from the first item type. 18. The apparatus of claim 16, wherein the plurality of machine learning-based classifiers comprises at least one of a naive Bayes classifier, a k-Nearest Neighbors (KNN) classifier, or a Perceptron classifier. 19. The apparatus of claim 16, wherein the operations further comprise: determining a third ranked list of item types based on the item record using a plurality of attribute-based classifiers. 20. The apparatus of claim 16, wherein the aggregating the first ranked list of item types and the second ranked list of item types into the combined ranked list of item types comprises: aggregating the first ranked list of item types, the second ranked list of item types, and the third ranked list of item types into the combined ranked list of item types.