Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The popular Naive Bayes (NB) algorithm is simple and fast.  present a new learning algorithm, Extended Bayes (EB), which is based on Naive Bayes. EB is still relatively simple, and achieves equivalent or higher accuracy than NB on a wide variety of the UC-Irvine datasets. EB is based on two ideas, which interact. The first is to find sets of seemingly dependent attributes and to add them as new attributes. The second idea is to exploit zeroes, that is, the negative evidence provided by attribute values that do not occur at all in particular classes in the training data. Zeroes are handled in Naive Bayes by smoothing. In contrast, EB uses them as evidence that a potential class labeling may be wrong.
['Bernard Rosell', 'Lisa Hellerstein']
I am writing a research paper on a idea Naïve Bayes with Higher Order Attributes
[{"from": "human", "value": "I am writing a research paper on a idea Na\u00efve Bayes with Higher Order Attributes"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The popular Naive Bayes (NB) algorithm is simple and fast.  present a new learning algorithm, Extended Bayes (EB), which is based on Naive Bayes. EB is still relatively simple, and achieves equivalent or higher accuracy than NB on a wide variety of the UC-Irvine datasets. EB is based on two ideas, which interact. The first is to find sets of seemingly dependent attributes and to add them as new attributes. The second idea is to exploit zeroes, that is, the negative evidence provided by attribute values that do not occur at all in particular classes in the training data. Zeroes are handled in Naive Bayes by smoothing. In contrast, EB uses them as evidence that a potential class labeling may be wrong."}]