Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Classification of Spam Emails through Hierarchical Clustering and
  Supervised Learning   Spammers take advantage of email popularity to send indiscriminately
unsolicited emails. Although researchers and organizations continuously develop
anti-spam filters based on binary classification, spammers bypass them through
new strategies, like word obfuscation or image-based spam. For the first time
in literature, we propose to classify spam email in categories to improve the
handle of already detected spam emails, instead of just using a binary model.
First, we applied a hierarchical clustering algorithm to create SPEMC-$11$K
(SPam EMail Classification), the first multi-class dataset, which contains
three types of spam emails: Health and Technology, Personal Scams, and Sexual
Content. Then, we used SPEMC-$11$K to evaluate the combination of TF-IDF and
BOW encodings with Na\"ive Bayes, Decision Trees and SVM classifiers. Finally,
we recommend for the task of multi-class spam classification the use of (i)
TF-IDF combined with SVM for the best micro F1 score performance, $95.39\%$,
and (ii) TD-IDF along with NB for the fastest spam classification, analyzing an
email in $2.13$ms.

29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
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The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: bayes-an