Content classification
Particular embodiments described herein provide for an electronic device that can be configured to analyze data using an ensemble and assign a classification to the data based, at least in part, on the results of the analyses using the ensemble. The ensemble can include one or more multinomial classifiers and each multinomial classifier can assign two or more classifications to the data.
1 . At least one machine readable medium comprising one or more instructions that when executed by at least one processor, cause the at least one processor to:
analyze data using an ensemble to produce results, wherein the ensemble includes one or more multinomial classifiers and each multinomial classifier can assign two or more classifications to the data;
assign one or more classifications to the data based, at least in part, on the results of the analyses using the ensemble; and
store the one or more classifications assigned to the data in memory.
2 . The at least one machine readable medium of claim 1 , wherein the data is located in an unclean dataset and is moved to a clean dataset after the classification is assigned.
3 . The at least one machine readable medium of claim 1 , comprising one or more instructions that when executed by at least one processor, further cause the at least one processor to:
determine a previously assigned classification for the data; and
compare the previously assigned classification to the assigned one or more classifications.
4 . The at least one machine readable medium of claim 1 , wherein the clean dataset includes a training dataset and a test dataset.
5 . The at least one machine readable medium of claim 4 , wherein the training dataset is used to create a new multinomial classifier and the new multinomial classifier is added to the ensemble.
6 . The at least one machine readable medium of claim 1 , wherein the ensemble includes a precision vector for each of the assigned one or more classifications.
7 . The at least one machine readable medium of claim 6 , wherein the precision vector is used to assign a confidence to each classification assigned to the data and the confidence can be compared to a threshold value.
8 . An apparatus comprising:
memory; and
a classification module configured to:
analyze data using an ensemble to produce results, wherein the ensemble includes one or more multinomial classifiers and each multinomial classifier can assign two or more classifications to the data; and
assign one or more classifications to the data based, at least in part, on the results of the analyses using the ensemble; and
store the classification in the memory.
9 . The apparatus of claim 8 , wherein the data is located in an unclean dataset and is moved to a clean dataset after the classification is assigned.
10 . The apparatus of claim 8 , wherein the classification module is further configured to:
determine a previously assigned classification for the data; and
compare the previously assigned classification to the assigned one or more classifications.
11 . The apparatus of claim 8 , wherein the clean dataset includes a training dataset and a test dataset.
12 . The apparatus of claim 11 , wherein the training dataset is used to create a new multinomial classifier and the new multinomial classifier is added to the ensemble.
13 . The apparatus of claim 8 , wherein the ensemble includes a precision vector for each of the assigned one or more classifications.
14 . The apparatus of claim 13 , wherein the precision vector is used to assign a confidence to each classification assigned to the data and the confidence can be compared to a threshold value.
15 . A method comprising:
analyzing data using an ensemble to produce results, wherein the ensemble includes one or more multinomial classifiers and each multinomial classifier can assign two or more classifications to the data;
assigning one or more classifications to the data based, at least in part, on the results of the analyses using the ensemble; and
storing the assigned one or more classifications in memory.
16 . The method of claim 15 , wherein the data is located in an unclean dataset and is moved to a clean dataset after the classification is assigned.
17 . The method of claim 15 , further comprising:
determining a previously assigned classification for the data; and
comparing the previously assigned classification to the assigned one or more classifications.
18 . The method of claim 15 , wherein the clean dataset includes a training dataset and a test dataset.
19 . The method of claim 15 , wherein the training dataset is used to create a new multinomial classifier and the new multinomial classifier is added to the ensemble.
20 . The method of claim 15 , wherein the ensemble includes a precision vector for each of the assigned one or more classifications.
21 . The method of claim 15 , wherein the precision vector is used to assign a confidence to each classification assigned to the data and the confidence can be compared to a threshold value.
22 . A system for content classification, the system comprising:
memory; and
a classification module configured for:
analyzing data using an ensemble to produce results, wherein the ensemble includes one or more multinomial classifiers and each multinomial classifier can assign two or more classifications to the data;
assigning a classification to the data based, at least in part, on the results of the analyses using the ensemble; and
storing the assigned classification in the memory.
23 . The system of claim 22 , wherein the classification module is further configured for:
determining a previously assigned classification for the data; and
comparing the previously assigned classification to the assigned classification.
24 . The system of claim 22 , wherein the clean dataset includes a training dataset and a test dataset.
25 . The system of claim 24 , wherein the training dataset is used to create a new multinomial classifier and the new multinomial classifier is added to the ensemble.