Description
Introduction
We’ve learned different ways of knowledge representation and problem-solving strategies to
answer different queries. In this project, we tried to answer query using a classification strategy.
That is given a set of classes, we seek to determine which class(es) a given object belongs to. To
conduct the classification task, we need to find a rule that captures a certain combination of
keywords that indicates a unique class. To achieve this, we can use hand-coded rules which have
good scaling properties, but creating and maintaining them over time is labor intensive. Or we can
build an expert system to create rule sets that will rival or exceed the accuracy of the automatically
generated classifiers as we plan to do in this project; however, it can be hard to find the expert with
this specialized skill.
Alternatively, we will practice to use statistical instead of knowledge representation strategy to
represent text information. A statistical text classification, we require a number of good example
documents (training documents) for each class. The need for manual classification is not
eliminated because the training documents come from a person who has labeled them, for example
to annotate an email as spam or ham. But labeling is arguably an easier task than writing rules.
A typical spam filter is a program that is used to detect unsolicited and unwanted email and
move those emails to a user’s spam inbox folder. Like other types of filtering programs, a spam
filter looks for certain criteria on which it bases judgments. Such as, the simplest and earliest can
be set to watch for particular words in the subject line of messages and to exclude these from the
user’s inbox, as shown in the Fig. 1.
Fig. 1 illustration of spam email filter.
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Methodology
1. Get to know the dataset
In this project, the dataset has been divided into training set, i.e., “train-mails” which contains
702 email text files, and test set, i.e., “test-mails” which contains 260 email text files. Note that, in
the dataset, the spam email samples are named with “spmXXX”. The detail information of this
dataset is listed in Table 1.
Table 1 Spam Filter Project Dataset
Dataset train-mails (702) test-mails (260)
ham emails spam emails ham emails spam emails
351 351 130 130
label 0 1 0 1
The name of this dataset is Ling-Spam Corpus which can be download here
https://www.stat.purdue.edu/~mdw/598/datasets.html. Note that, the provided dataset in this
project is a preselected subset of the original dataset.
In this project, we use 𝑑 ∈ 𝐷 represents one email text file, where 𝐷 is all the email files. 𝐶 =
{𝑐1, 𝑐2
} = {0, 1} represent ham, spam classes, respectively. Then for the email files in the training
set we have the following file and label pair,
< 𝑑, 𝑐 >
For example,
< 𝑑, 𝑐 >= < listserv 𝑖𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑐𝑜𝑛𝑓𝑒𝑟𝑒𝑛𝑐𝑒 … 𝑝𝑙𝑒𝑎𝑠𝑒 𝑐𝑜𝑛𝑡𝑎𝑐𝑡 𝑜𝑟𝑔𝑎𝑛𝑖𝑧𝑒𝑟, ℎ𝑎𝑚 >
To implement this in Python, we have to read all email files in, and then extract content in each
file and store it. For this purpose, we can define a method named read_file.
2. Feature Extraction
Text is very common information in our daily life, however it is hard to direct use text conduct
computing in programs. You can think of text as a sequence of characters, words, phrases and
named entities, sentences, paragraphs, and so on. It seems natural to think of a text as a sequence
of words. A word is a meaningful sequence of characters, and in English we can split a sentence
by spaces or punctuation, for example,
Input Text: “anyone point book book article causative construction Korean book”
Output Words: anyone point book article causative construction korean
One thing you may noticed that the input text in our dataset is a little different from the email
that we see. This because these files are preprocessed by using basic natural language
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preprocessing technologies, such as Tokenization, Stemming, and Lemmatization. If you are
interested, you are welcome to explore more.
In this project, we use the bag of words (BOW) feature for the text. BOW try to count
occurrences of a particular word/token in our text, then the higher the occurrence the more
important word in the text. For each token or word, we will have a feature column, this is called
text vectorization. The problem of the BOW is that we loose word order, hence the name “bag of
words”.
For example, the input text above can be represented by a BOW vector
anyone point book article causative construction korean
1 1 3 1 1 1 1
Here the output words together work as a dictionary for us to create the vector. For the spam
email filter, ham emails and spam emails may use different words to covey information. For
example, for spam emails there may be words heavily occurrence, such as, discount, money,
shopping etc., while for ham emails there may be words heavily occurrence, such as, work, time,
meetings, and so on. So, if we can take all unique words from the training set to form a dictionary,
then we can use text vectorization to represent each of the email file. However, the dictionary may
contain too many unique words, and some of these words may not convey useful meanings, such
as anyone, point, is, etc. In this case, we sort the words in the dictionary based on their frequency,
and choose 𝑛 of them to form a new dictionary which contains a subset of the original dictionary.
In this project we choose 𝑛 = 3000 most important words in the dictionary. Then we can use one
text vector to represent each email file.
3. Classification Model
With the provided training data and related label, we then wish to learn a classifier or
classification function 𝑓that maps email file to corresponding class label:
𝑓:𝐷 → 𝐶
This type of learning is called supervised learning, because a supervisor (the human who defines
the classes and labels training documents) serves as a teacher directing the learning process. Once
we have learned the classification function 𝑓, we can apply it to the test set (or test data). Our goal
in this project is to have a classifier to get high accuracy on test data or new data. Accuracy means
the classifier successfully classifies the test sample number over all the test set samples. Normally,
it is to achieve high accuracy on the training set (e.g., we can simply memorize the labels). But
high accuracy on the training set in general does not mean that the classifier will work well on new
or unseen data in an application. However, when we use the training set to learn a classifier for
test data, we make the assumption that training data and test data are similar or from the same
distribution.
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In this project, we mainly focused on two types of classification model, i.e., Multinomial Naïve
Bayes and Bernoulli Naïve Bayes. In addition, if you’re interested in Gaussian Naïve Bayes
statistic model, you can opt to finish the bonus project task.
3.1 Multinomial Naïve Bayes (NB) Statistic Model
The first supervised learning method we used is the multinomial Naive Bayes or multinomial
NB model, a probabilistic learning method. It is relatively suitable for text related classification.
The original probability equation is:
𝑃(ℎ𝑖
|𝐸) =
𝑃(𝐸|ℎ𝑖
)∗𝑃(ℎ𝑖
)
𝑃(𝐸)
(1)
𝐸 is the given evidences set (which is the features in a specific classification problem); ℎ𝑖
is
one hypothesis in H (H is the set of all hypotheses, which is all classes in a specific classification
problem). 𝑃(𝐸|ℎ𝑖
) is the conditional probability of term 𝐸 belongs to class ℎ𝑖
. 𝑃(ℎ𝑖
) is the prior
probability of an email text belonging to class ℎ𝑖
.
We assume that P(E) is the same for all the emails, we can approximate the equation using:
𝑃(ℎ𝑖
|𝐸) = 𝑃(𝐸|ℎ𝑖
) ∗ 𝑃(ℎ𝑖) (2)
Since the given evidence 𝐸 contains a sequence of 𝑛𝐸 words 𝑒1,𝑒2, … ,𝑒𝑛𝐸
, these words are
independent from each other in BOW, therefore Eq. (2) can be rewrite as
𝑃(ℎ𝑖
|𝐸) = ∏ 𝑃(𝑒𝑖
|ℎ𝑖 1≤𝑘≤𝑛𝐸
) ∗ 𝑃(ℎ𝑖) (3)
where 𝑒𝑘 is one evidence (i.e., a word term) in 𝐸 that are part of the vocabulary we use for
classification. 𝑛𝐸 is the number of evidences in 𝐸 (i.e., total words term in an email file). For
example, the < 𝑒1,𝑒2, … ,𝑒𝑛𝐸
> for the input text in Section 2 might be <
𝑎𝑛𝑦𝑜𝑛𝑒, 𝑝𝑜𝑖𝑛𝑡, 𝑏𝑜𝑜𝑘, 𝑎𝑟𝑡𝑖𝑐𝑙𝑒, 𝑐𝑎𝑢𝑠𝑎𝑡𝑖𝑣𝑒, 𝑐𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑖𝑜𝑛, 𝑘𝑜𝑟𝑒𝑎𝑛 > with 𝑛𝐸 = 7.
𝑃(ℎ𝑖
|𝐸) means given a feature vector 𝐸 of an email text file, we want to calculate the
probability that this feature vector belongs to ham 𝑃(ℎ0 = ℎ𝑎𝑚|𝐸) and the probability that this
feature vector belongs to spam 𝑃(ℎ1 = 𝑠𝑝𝑎𝑚|𝐸). In this project, our goal is to find the best class
for the email file. The best class in NB classification is the most likely or maximum a posteriori
(MAP) class ℎ𝑏𝑒𝑠𝑡:
ℎ𝑏𝑒𝑠𝑡 = argmax
ℎ𝑖∈𝐻
∏ 𝑃(𝑒𝑘
|ℎ𝑖
) 1≤𝑘≤𝑛𝐸
∗ 𝑃(ℎ𝑖) (4)
There is one problem in this approximation Eq. (4), that is the multiplicative of probabilities
can cause the float point overflow problem. So, we perform the computation by adding logarithms
of probabilities instead of multiplying probabilities to calculate the parameterized probabilistic
distribution:
ℎ𝑏𝑒𝑠𝑡 = argmax
ℎ𝑖∈𝐻
∑ log 𝑃(𝑒𝑘
|ℎ𝑖
) 1≤𝑘≤𝑛𝐸 + log 𝑃(ℎ𝑖
) (5)
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Note that the class with the highest log probability score is still the most probable, and the
logarithm function is monotonic. The interpretation of Eq. (5) are: the conditional probability
𝑃(𝑒𝑘
|ℎ𝑖
) indicates how good a word is for class ℎ𝑖
; the priori probability 𝑃(ℎ𝑖
) is a weight that
indicates the relative frequency of class ℎ𝑖
; the sum of the log prior and term weights is then a
measure of how much evidence there is for the email file being in the class, and the Eq. (5) will
select the class for which we have the most evidence.
The question is how do we estimate the terms 𝑃(𝑒𝑘
|ℎ𝑖
) and 𝑃(ℎ𝑖
)? We can estimate them from
the training set:
For the prior estimate is:
𝑃(ℎ𝑖
) = 𝑁ℎ𝑖
/𝑁 (6)
where 𝑁ℎ𝑖
is the number of email files in class ℎ𝑖
in the training set and 𝑁 is the total number of
email files in the training set.
For the conditional probability 𝑃(𝑒𝑘
|ℎ𝑖
), we estimate it as the relative frequency of term 𝑒𝑘 in
files or documents belonging to class ℎ𝑖
,
𝑃(𝑒𝑘
|ℎ𝑖
) =
𝑂ℎ𝑖
,𝑒𝑘
∑ 𝑂ℎ𝑖
𝑒𝑡 ∈𝐷𝑖𝑐𝑡𝑖𝑜𝑛𝑎𝑟𝑦 ,𝑒𝑡
=
𝑂ℎ𝑖
,𝑒𝑘
+ 𝛼
∑ 𝑂ℎ𝑖
𝑒𝑡 ∈𝐷𝑖𝑐𝑡𝑖𝑜𝑛𝑎𝑟𝑦 ,𝑒𝑡
+𝑛∗ 𝛼
(7)
where 𝑂𝑒𝑘
is the number of occurrences of 𝑒𝑘 in the training email files from class ℎ𝑖
.
∑ 𝑂ℎ𝑖 𝑒𝑡 ∈𝐷𝑖𝑐𝑡𝑖𝑜𝑛𝑎𝑟𝑦 ,𝑒𝑡
represents the sum of occurrences of every word in the dictionary from class
ℎ𝑖
in the training email files. As defined in Section 2, 𝑛 is the number of words in the dictionary.
Note that, here 𝛼 is a smooth variable used to eliminate zeros, and we normally set 𝛼 = 1, called
add-one smoothing.
To implement multinomial NB model in Python:
1. Preparing the training data
a. Store the email files based on their class labels (either ham or spam)
b. For each class, record the number of files and the distinct or unique words in each
document
c. Generate the feature matrix for each file, the occurrence of the word in the
dictionary is recorded, then the feature matrix has the following format
feature matrix=[
𝑤𝑜𝑟𝑑1 𝑤𝑜𝑟𝑑2 …
𝑓𝑖𝑙𝑒1 𝑂11 𝑂12 …
𝑓𝑖𝑙𝑒2 𝑂21 𝑂22 …
… … … …
]
2. Generate the parameterized distribution based on the feature matrix for each class to form
the logarithm matrix
feature_log_prob=[
𝑤𝑜𝑟𝑑1 𝑤𝑜𝑟𝑑2 …
𝑠𝑝𝑎𝑚 log 𝑝(𝑒1
|𝑠𝑝𝑎𝑚) log 𝑝(𝑒2
|𝑠𝑝𝑎𝑚) …
ℎ𝑎𝑚 log 𝑝(𝑒1
|ℎ𝑎𝑚) log 𝑝(𝑒2
|ℎ𝑎𝑚) …
]
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then the multinomial NB model parameters can be achieved.
To test the multinomial NB model in Python:
To verify the effectiveness of the build multinomial NB model, we need to perform the
following steps:
1. For each email file in the test set, we calculate its related feature vector based on the word
in the dictionary.
feature vector of the test email file = [
𝑤𝑜𝑟𝑑1 𝑤𝑜𝑟𝑑2 ⋯
𝑡𝑒𝑠𝑡𝑓𝑖𝑙𝑒 𝑂𝑡1 𝑂𝑡2 ⋯
]
2. Do the elementwise product with feature_log_prob for both spam and ham class followed
by the equation in (5).
For ham class probability:
ℎ0_𝑡𝑒𝑠𝑡= ∑ log 𝑃(𝑒𝑘
|ℎ0 1≤𝑘≤𝑛𝐸
) ∗ 𝑂𝑡𝑒𝑘 + log 𝑃(ℎ0
)
For spam class probability:
ℎ1_𝑡𝑒𝑠𝑡= ∑ log 𝑃(𝑒𝑘
|ℎ1 1≤𝑘≤𝑛𝐸
) ∗ 𝑂𝑡𝑒𝑘 + log 𝑃(ℎ1
)
If ℎ0_𝑡𝑒𝑠𝑡 > ℎ1_𝑡𝑒𝑠𝑡, then the test email file belongs to the ham class;
Otherwise, the test email file belongs to the spam class.
3.2 Bernoulli Naïve Bayes (NB) Statistic Model (optional)
The second way to set up an NB classifier is to use the Bernoulli NB model. The Bernoulli
model has the same time complexity as the multinomial model. However, the Bernoulli NB model
is a binary independence model, which generates an indicator for each term of the vocabulary,
either 1 indicating presence of the term in the document or 0 indicating absence. The different
generation models imply different estimation strategies and different classification rules. More
information about the Bernoulli model can be find here
(https://en.wikipedia.org/wiki/Bernoulli_distribution ).
In general, in this project most of the parts of calculation of Bernoulli NB model is similar to
Multinomial NB model. But still there are some differences. The differences are:
a) We need to transfer each term of the feature matrix to 1 and 0 since it follows the
Bernoulli distribution. If the original entry is non-0 then it is 1 or it is 1.
b) feature matrix=[
𝑤𝑜𝑟𝑑1 𝑤𝑜𝑟𝑑2 …
𝑓𝑖𝑙𝑒1 1 0 …
𝑓𝑖𝑙𝑒2 0 1 …
… … … …
]
c) When conducting the prediction, the multiplication will be changed to:
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spam = ∑ log 𝑝(𝑒𝑘
|𝑠𝑝𝑎𝑚) ∗ (0 𝑜𝑟 1) + (1 − log 𝑝(𝑒𝑘 1≤𝑘≤𝑛
|𝑠𝑝𝑎𝑚)) ∗ (𝑓𝑙𝑖𝑝 𝑜𝑓 0 𝑜𝑟 1)
𝐸 +
log 𝑝(ℎ1
)
ham = ∑ log 𝑝(𝑒𝑘
|ℎ𝑎𝑚) ∗ (0 𝑜𝑟 1) + (1 − log 𝑝(𝑒𝑘 1≤𝑘≤𝑛
|ℎ𝑎𝑚)) ∗ (𝑓𝑙𝑖𝑝 𝑜𝑓 0 𝑜𝑟 1)
𝐸 +
log 𝑝(ℎ0
)
3.3 Gaussian Naïve Bayes Statistic Model (Optional)
Though Gaussian Naive Bayes is suitable for continuous data, we want to implement here to
see the effect of this model. A general Gaussian distribution is used:
(8)
What we will do here is:
a) Preparing the training data and do statistical according to class and features. This
process is similar to previous two algorithms.
b) For a Gaussian model, we want to get the mean and standard deviation:
Mean 𝜇 =
∑𝑗∈𝑁𝑒
𝑁𝑗
𝑘
𝑁𝑒𝑘
; Std 𝜎 = √
∑ (𝑁𝑗−𝜇)
2
𝑗∈𝑁𝑒𝑘
𝑁𝑒𝑘
(9)
This model is for each feature (in our instance for each word) in one class.
c) Prediction is simple sum all features in testing sample according to equation (8).
For each entry in testing feature matrix set 𝑥 = 𝑒𝑛𝑡𝑟𝑦 and calculate the result.
Project Requirement
In this project, we need to use different statistical models that can be used to perform spam
email classification task. The detailed requirements are:
1. Using multinomial NB model to solve the spam email filter problem (80%)
As stated in section 3.1.
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2. Result analysis. (10%)
Please test your probability model performance. Try to understand the computation cost,
accuracy. Write down your detailed analysis and conclusion.
3. Project Report (10%)
You have to write a project report for this project. You have to complete the template and submit
it together with your project source code. The length of the report must longer than 1 page. The
template of the project report is attached in the project zip file.
4. Bonus Works (Extra 30%)
A. Using Bernoulli Naïve Bayes model to solve the spam email filter problem (10%)
As stated in section 3.2.
B. Using Gaussian Naïve Bayes Statistic Model to solve the spam email filter problem. (10%)
As stated in section 3.3.
C. Increasing the training and testing set size and see the results. Is increasing the size of samples
have a positive influence on the accuracy. (10%)
Hand-In Requirements
(1) The starter code is provided in Python language. You must follow the provided starter code
to finish this project. There are two reasons for this:
a. To prevent plagiarism;
b. Reading and understanding other people’s code should be one the ability of CS students.
Note that if your team choose to use Java to implement this project, please contact me for the
information about the starter code.
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(2) If you completed this project, you need to hand in the source code been completed by you or
your group. Your source code compressed to a single ZIP file. The name of the code archive
should be SEFilter_YourTeamname.zip.
The code should be well commented, and it should be easy to see the correspondence between
what’s in the code and what’s in the report. You don’t need to include executable or various
supporting files (e.g., utility libraries) whose content is irrelevant to the assignment. If the
instructor finds it necessary to run your code in order to evaluate your solution, the instructor
will get in touch with you.
(3) A project report in PDF format
• The report should briefly describe your implemented solution and fully answer all the
questions posed above. Remember: you will not get credit for any solutions you have
obtained, but not included in the report.
• All group reports need to include paragraph of individual contribution, i.e., which group
member was responsible for which parts of the solution and submitted material. The
instructor reserves the right to contact group members individually to verify this
information.
• Describe the solution and compare the number of attempted search steps and execution
time for your uninformed search method and informed search method implementations.
• The name of the report file should be SEFilter_YourTeamname.pdf. Don’t forget to
include the names of all group members and the number of credit units at the top of the
report. (You can find the report template on the provided zip file together with the starter
code.)
• Finally, which part you think can be improved. Or the challenges you encountered during
this project.
Note that, the instructor reserves the right to give bonus points for any advanced exploration
or especially challenging or creative solutions that you implement. If you submit any work
for bonus points, be sure it is clearly indicated in your report.
WARNING: You will not get credit for any solutions that you have obtained, but not
included in your report!
