Naïve Bayes is a probabilistic machine learning algorithm that can be used in a wide variety of classification tasks. Typical applications include filtering spam, classifying documents, sentiment prediction etc. Naïve Bayes classifiers are a popular statistical technique of email-filtering. Bayes Rule P (H | E) = (P (E | H))/(P (E)) × P (H).

1. SEMINAR ON
“NAÏVE BAYES FOR SPAM
FILTERING”
Presented by:
Asrarulhaq Maktedar

2. Introduction:
• Naïve Bayes is a probabilistic machine
learning algorithm that can be used in wide
variety of classification tasks.
• Typical applications include filtering spam,
classifying documents, sentiment
prediction etc.
• Naïve Bayes classifiers are a popular
statistical technique of email-filtering.
• Bayes Rule P (H | E) =
P (E | H)
P (E)
× P (H).

3. Naïve
Bayesian
Algorithm
P(w) can be interpreted as the probability that a
randomly chosen email containing the word w is
Spam.
For each word in the mail, Calculate the following:
S(w)=(number of Spam
emails containing the
word)/(total number of
Spam emails)
H(w)=(number of Ham
emails containing the
word)/(total number of
Ham emails)
P(w)=S(w)/(S(w)+H(w))
Naïve Bayesian method is used for the learning
process. Analyze a mail to calculate its probability
of being a Spam using individual characteristic of
words in the mail.

4. Genetic Algorithm
A mail can be divided into three parts:
• Body
• From
• Subject
Genetic Algorithm can be used to get an appropriate
weights say α, β and γ for “body” part, “from” part and
“subject” part.
IFinal= α*IBody+ β*IFrom+ γ*ISubject
The overall accuracy is a function of α, β and γ. Genetic
Algorithm maximize the above function.

5. Example: School
• Consider a school with a total population of 100 persons.
These 100 persons can be seen either as ‘Students’ and
‘Teachers’ or as a population of ‘Males’ and ‘Females’.
• The 100 people, what is the conditional probability that a
certain member of the school is a ‘Teacher’ given that he
is a ‘Man’?

6. References:
• https://www.machinelearningplus.com
• https://en.m.wikipedia.org
• https://cwiki.apache.org