A method for multi-instance learning (MIL)-based classification of a streaming input is described. The method includes running a first biased MIL model using extracted features from a subset of instances received in the streaming input to obtain a first classification result. The method also includes running a second biased MIL model using the extracted features to obtain a second classification result. The first biased MIL model is biased opposite the second biased MIL model. The method further includes classifying the streaming input based on the classification results of the first biased MIL model and the second biased MIL model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for multi-instance learning (MIL)-based classification of a streaming input, comprising: running a first biased MIL model using extracted features from a subset of instances received in the streaming input to obtain a first classification result; running a second biased MIL model using the extracted features to obtain a second classification result, wherein the first biased MIL model is biased opposite the second biased MIL model; and classifying the streaming input based on the classification results of the first biased MIL model and the second biased MIL model. 2. The method of claim 1, wherein the first biased MIL model and the second biased MIL model are each biased towards opposite binary classification types. 3. The method of claim 1, wherein the first biased MIL model is generated using a first training set that is biased toward a first classification and the second biased MIL model is generated using a second training set that is biased toward a second classification. 4. The method of claim 1, wherein when the classification results of the first biased MIL model and the second biased MIL model indicate a same classification for the streaming input, then the classification of the streaming input is considered accurate. 5. The method of claim 1, wherein when the classification results of the first biased MIL model and the second biased MIL model indicate a different classification for the streaming input, then the classification of the streaming input is considered inaccurate. 6. The method of claim 5, further comprising: running an unbiased MIL model using the extracted features; and classifying the streaming input based on a classification result of the unbiased MIL model. 7. The method of claim 5, further comprising: obtaining one or more additional instances of the streaming input; running the first biased MIL model and the second biased MIL model using extracted features from the additional instances of the streaming input to obtain additional classification results; and classifying the streaming input based on the additional classification results of the first biased MIL model and the second biased MIL model. 8. The method of claim 1, further comprising updating the first biased MIL model and the second biased MIL model based on the classification results. 9. The method of claim 1, wherein the streaming input comprises a webpage and the instances comprise JavaScript blocks within the webpage. 10. The method of claim 9, wherein a partial subset of JavaScript blocks in the webpage are used to run the first biased MIL model and the second biased MIL model to classify the webpage as either benign or malicious. 11. The method of claim 9, wherein the first biased MIL model is biased toward a benign classification for the webpage and the second biased MIL model is biased toward a malicious classification for the webpage. 12. The method of claim 9, further comprising allowing the webpage to load when both the first biased MIL model and the second biased MIL model indicate that the webpage is benign. 13. The method of claim 9, further comprising preventing the webpage from loading when both the first biased MIL model and the second biased MIL model indicate that the webpage is malicious. 14. The method of claim 1, further comprising performing higher order classification of the streaming input beyond binary classification. 15. The method of claim 14, wherein performing the higher order classification comprises: running a plurality MIL model sets using the extracted features, wherein a MIL model set includes two MIL models each biased towards two opposite binary classification types; and combining classification results of the plurality MIL model sets to obtain a final classification. 16. The method of claim 14, wherein performing the higher order classification comprises: running three or more biased MIL models that are each biased towards a particular classification using the extracted features; combining classification results from the three or more biased MIL models; and determining a final classification when a majority of the biased MIL models indicate the same classification result, wherein the final classification is deemed strong and finalized. 17. The method of claim 16, wherein if there is no single majority of the biased MIL models that produce the same classification result, the final classification is obtained by choosing a highest frequency classification result. 18. An electronic device configured for multi-instance learning (MIL)-based classification of a streaming input, comprising: a memory; and a processor in communication with the memory, the processor configured to: run a first biased MIL model using extracted features from a subset of instances received in the streaming input to obtain a first classification result; run a second biased MIL model using the extracted features to obtain a second classification result, wherein the first biased MIL model is biased opposite the second biased MIL model; and classify the streaming input based on the classification results of the first biased MIL model and the second biased MIL model. 19. The electronic device of claim 18, wherein the first biased MIL model is generated using a first training set that is biased toward a first classification and the second biased MIL model is generated using a second training set that is biased toward a second classification. 20. The electronic device of claim 18, wherein when the classification results of the first biased MIL model and the second biased MIL model indicate a same classification for the streaming input, then the classification of the streaming input is considered accurate. 21. The electronic device of claim 18, wherein the streaming input comprises a webpage and the instances comprise JavaScript blocks within the webpage. 22. The electronic device of claim 21, wherein a partial subset of JavaScript blocks in the webpage are used to run the first biased MIL model and the second biased MIL model to classify the webpage as either benign or malicious. 23. A non-transitory tangible computer readable medium for multi-instance learning (MIL)-based classification of a streaming input, the computer readable medium storing computer executable code, comprising: code for causing an electronic device to run a first biased MIL model using extracted features from a subset of instances received in the streaming input to obtain a first classification result; code for causing the electronic device to run a second biased MIL model using the extracted features to obtain a second classification result, wherein the first biased MTh model is biased opposite the second biased MTh model; and code for causing the electronic device to classify the streaming input based on the classification results of the first biased MIL model and the second biased MIL model. 24. The computer readable medium of claim 23, wherein the first biased MIL model is generated using a first training set that is biased toward a first classification and the second biased MIL model is generated using a second training set that is biased toward a second classification. 25. The computer readable medium of claim 23, wherein when the classification results of the first biased MIL model and the second biased MIL model indicate a same classification for the streaming input, then the classification of the streaming input is considered accurate. 26. The computer readable medium of claim 23, wherein the streaming input comprises a webpage and the instances comprise JavaScript blocks within the webpage. 27. An apparatus for multi-instance learning (MIL)-based classification for a streaming input, comprising: means for running a first biased MIL model using extracted features from a subset of instances received in the streaming input to obtain a first classification result; means for running a second biased MIL model using the extracted features to obtain a second classification result, wherein the first biased MIL model is biased opposite the second biased MIL model; and means for classifying the streaming input based on the classification results of the first biased MIL model and the second biased MIL model. 28. The apparatus of claim 27, wherein the first biased MIL model is generated using a first training set that is biased toward a first classification and the second biased MIL model is generated using a second training set that is biased toward a second classification. 29. The apparatus of claim 27, wherein when the classification results of the first biased MIL model and the second biased MIL model indicate a same classification for the streaming input, then the classification of the streaming input is considered accurate. 30. The apparatus of claim 27, wherein the streaming input comprises a webpage and the instances comprise JavaScript blocks within the webpage.