<doi_batch xmlns="http://www.crossref.org/schema/4.4.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" version="4.4.0"><head><doi_batch_id>085565e5-4c2b-4e2e-af53-7f348ce3c67b</doi_batch_id><timestamp>20210127060103197</timestamp><depositor><depositor_name>wsea</depositor_name><email_address>mdt@crossref.org</email_address></depositor><registrant>MDT Deposit</registrant></head><body><journal><journal_metadata language="en"><full_title>WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL</full_title><issn media_type="electronic">2224-2856</issn><issn media_type="print">1991-8763</issn><archive_locations><archive name="Portico"/></archive_locations><doi_data><doi>10.37394/23203</doi><resource>http://wseas.org/wseas/cms.action?id=4073</resource></doi_data></journal_metadata><journal_issue><publication_date media_type="online"><month>1</month><day>7</day><year>2021</year></publication_date><publication_date media_type="print"><month>1</month><day>7</day><year>2021</year></publication_date><journal_volume><volume>16</volume><doi_data><doi>10.37394/23203.2021.16</doi><resource>https://wseas.org/wseas/cms.action?id=23276</resource></doi_data></journal_volume></journal_issue><journal_article language="en"><titles><title>Evaluation of the Performance for Popular Three Classifiers on Spam Email without using FS methods</title></titles><contributors><person_name sequence="first" contributor_role="author"><given_name>Ghada</given_name><surname>AL-Rawashdeh</surname><affiliation>Ocean Engineering Technology and Informatics Dept, Universiti Malaysia Terengganu, Malaysia</affiliation></person_name><person_name sequence="additional" contributor_role="author"><given_name>Rabiei Bin</given_name><surname>Mamat</surname><affiliation>Ocean Engineering Technology and Informatics Dept, Universiti Malaysia Terengganu, Malaysia</affiliation></person_name><person_name sequence="additional" contributor_role="author"><given_name>Jawad Hammad</given_name><surname>Rawashdeh</surname><affiliation>Computing and Informatics Dept, Saudi Electronic University,  Saudi Arabia</affiliation></person_name></contributors><jats:abstract xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1"><jats:p>Email is one of the most economical and fast communication means in recent years; however, there has been a high increase in the rate of spam emails in recent times due to the increased number of email users. Emails are mainly classified into spam and non-spam categories using data mining classification techniques. This paper provides a description and comparative for the evaluation of effective classifiers using three algorithms - namely k-nearest neighbor, Naive Bayesian, and support vector machine. Seven spam email datasets were used to conducted experiment in the MATLAB environment without using any feature selection method. The simulation results showed SVM classifier to achieve a better classification accuracy compared to the K-NN and NB.</jats:p></jats:abstract><publication_date media_type="online"><month>1</month><day>27</day><year>2021</year></publication_date><publication_date media_type="print"><month>1</month><day>27</day><year>2021</year></publication_date><pages><first_page>121</first_page><last_page>132</last_page></pages><ai:program xmlns:ai="http://www.crossref.org/AccessIndicators.xsd" name="AccessIndicators"><ai:free_to_read start_date="2021-01-27"/><ai:license_ref applies_to="am" start_date="2021-01-27">https://www.wseas.org/multimedia/journals/control/2021/a185103-976.pdf</ai:license_ref></ai:program><archive_locations><archive name="Portico"/></archive_locations><doi_data><doi>10.37394/23203.2021.16.9</doi><resource>https://www.wseas.org/multimedia/journals/control/2021/a185103-976.pdf</resource></doi_data><citation_list><citation key="ref0"><unstructured_citation>Zdziarski, J.A., 2005. Ending spam: Bayesian content filtering and the art of statistical language classification. No Starch Press. </unstructured_citation></citation><citation key="ref1"><doi>10.5267/j.msl.2020.1.010</doi><unstructured_citation>Al-Gasawneh, J., &amp; Al-Adamat, A. (2020). The mediating role of e-word of mouth on the relationship between content marketing and green purchase intention. Management Science Letters, 10(8), 1701-1708. </unstructured_citation></citation><citation key="ref2"><doi>10.3115/1621804.1621819</doi><unstructured_citation>El Kourdi, M., Bensaid, A. and Rachidi, T.E., 2004, August. Automatic Arabic document categorization based on the Naïve Bayes algorithm. In proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages (pp. 51-58). Association for Computational Linguistics. </unstructured_citation></citation><citation key="ref3"><unstructured_citation>Al-Harbi, S., Almuhareb, A., Al-Thubaity, A., Khorsheed, M.S. and Al-Rajeh, A., 2008. Automatic Arabic text classification. </unstructured_citation></citation><citation key="ref4"><doi>10.1016/j.neucom.2017.04.053</doi><unstructured_citation>Mafarja, Majdi M., and Seyedali Mirjalili. "Hybrid Whale Optimization Algorithm with simulated annealing for feature selection." Neurocomputing 260 (2017): 302-312. </unstructured_citation></citation><citation key="ref5"><unstructured_citation>Eyheramendy, S., Lewis, D.D. and Madigan, D., 2003. On the naive bayes model for text categorization. </unstructured_citation></citation><citation key="ref6"><doi>10.1007/3-540-45268-0_6</doi><unstructured_citation>Galavotti, L., Sebastiani, F. and Simi, M., 2000, September. Experiments on the use of feature selection and negative evidence in automated text categorization. In International Conference on Theory and Practice of Digital Libraries (pp. 59-68). Springer, Berlin, Heidelberg. </unstructured_citation></citation><citation key="ref7"><doi>10.13057/psnmbi/m010614</doi><unstructured_citation>Duwiri, M.U.C., 2007. KERAGAMAN JENIS DAN PENYEBARAN KUPU–KUPU SUPERFAMILI PAPILIONOIDEA ORDO LEPIDOPTERA DI KAMPUNG MOKWAM DISTRIK MINYAMBOU KABUPATEN MANOKWARI (Doctoral dissertation, Universitas Negeri Papua). </unstructured_citation></citation><citation key="ref8"><unstructured_citation>Forman, G., 2003. An extensive empirical study of feature selection metrics for text classification. Journal of machine learning research, 3(Mar), pp.1289-1305. </unstructured_citation></citation><citation key="ref9"><doi>10.1007/s10115-004-0177-2</doi><unstructured_citation>Fragoudis, D., Meretakis, D. and Likothanassis, S., 2005. Best terms: an efficient feature-selection algorithm for text categorization. Knowledge and Information Systems, 8(1), pp.16-33. </unstructured_citation></citation><citation key="ref10"><unstructured_citation>Gupta, A.K. and Nagar, D.K., 2018. Matrix variate distributions. Chapman and Hall/CRC. </unstructured_citation></citation><citation key="ref11"><doi>10.1007/978-981-10-6872-0_65</doi><unstructured_citation>Sarkar, J.L., Panigrahi, C.R., Pati, B., Trivedi, R. and Debbarma, S., 2018. E2G: A game theory-based energy efficient transmission policy for mobile cloud computing. In Progress in Advanced Computing and Intelligent Engineering (pp. 677-684). Springer, Singapore. </unstructured_citation></citation><citation key="ref12"><unstructured_citation>Ozgur, A., 2004. Supervised and unsupervised machine learning techniques for text document categorization. Unpublished Master’s Thesis, İstanbul: Boğaziçi University. </unstructured_citation></citation><citation key="ref13"><doi>10.1093/nar/gkw1118</doi><unstructured_citation>Wang, Y., Bryant, S.H., Cheng, T., Wang, J., Gindulyte, A., Shoemaker, B.A., Thiessen, P.A., He, S. and Zhang, J., 2016. Pubchem bioassay: 2017 update. Nucleic acids research, 45(D1), pp.D955-D963. </unstructured_citation></citation><citation key="ref14"><doi>10.1101/128835</doi><unstructured_citation>Jain, M., Koren, S., Miga, K.H., Quick, J., Rand, A.C., Sasani, T.A., Tyson, J.R., Beggs, A.D., Dilthey, A.T., Fiddes, I.T. and Malla, S., 2018. Nanopore sequencing and assembly of a human genome with ultra-long reads. Nature biotechnology, 36(4), p.338. </unstructured_citation></citation><citation key="ref15"><doi>10.1016/j.comcom.2018.09.005</doi><unstructured_citation>Saha, S.K., Ghasempour, Y., Haider, M.K., Siddiqui, T., De Melo, P., Somanchi, N., Zakrajsek, L., Singh, A., Shyamsunder, R., Torres, O. and Uvaydov, D., 2019. X60: A programmable testbed for wideband 60 ghz wlans with phased arrays. Computer Communications, 133, pp.77-88. </unstructured_citation></citation><citation key="ref16"><unstructured_citation>Parveen, P., 2016. Prof. Gambhir Halse, “Spam mail detection using classification”. International Journal of Advanced Research in Computer and Communication Engineering, 5(6). </unstructured_citation></citation><citation key="ref17"><doi>10.17485/ijst/2016/v9i39/90599</doi><unstructured_citation>DeepaLakshmi, S. and Velmurugan, T., 2016. Empirical study of feature selection methods for high dimensional data. Indian Journal of Science and Technology, 9, p.39. </unstructured_citation></citation><citation key="ref18"><unstructured_citation>Pletcher, R.H., Tannehill, J.C. and Anderson, D., 2012. Computational fluid mechanics and heat transfer. CRC press. </unstructured_citation></citation><citation key="ref19"><unstructured_citation>Wijayawardene, N.N., Crous, P.W., Kirk, P.M., Hawksworth, D.L., Boonmee, S., Braun, U., Dai, D.Q., D’souza, M.J., Diederich, P., Dissanayake, A. and Doilom, M., 2014. Naming and outline of Dothideomycetes–2014 including proposals for the protection or Reference suppression of generic names. Fungal Diversity, 69(1), pp.1-55. </unstructured_citation></citation><citation key="ref20"><doi>10.2307/2411219</doi><unstructured_citation>Mitchell, R.J., 1997. Effects of pollen quantity on progeny vigor: evidence from the desert mustard Lesquerella fendleri. Evolution, 51(5), pp.1679-1684. </unstructured_citation></citation><citation key="ref21"><unstructured_citation>Yang, Y. and Liu, X., 1999, August. A reexamination of text categorization methods. In Sigir (Vol. 99, No. 8, p. 99). </unstructured_citation></citation><citation key="ref22"><doi>10.1016/s0891-5849(98)00315-3</doi><unstructured_citation>Re, R., Pellegrini, N., Proteggente, A., Pannala, A., Yang, M. and Rice-Evans, C., 1999. Antioxidant activity applying an improved ABTS radical cation decolorization assay. Free radical biology and medicine, 26(9- 10), pp.1231-1237. </unstructured_citation></citation><citation key="ref23"><doi>10.1007/978-3-642-23496-5_13</doi><unstructured_citation>Mccord, M. and Chuah, M., 2011, September. Spam detection on twitter using traditional classifiers. In international conference on Autonomic and trusted computing (pp. 175-186). Springer, Berlin, Heidelberg. </unstructured_citation></citation><citation key="ref24"><unstructured_citation>Drucker, H., Burges, C.J., Kaufman, L., Smola, A.J. and Vapnik, V., 1997. Support vector regression machines. In Advances in neural information processing systems (pp. 155-161). </unstructured_citation></citation><citation key="ref25"><unstructured_citation>Burges, C.J., 1998. A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2), pp.121-167. </unstructured_citation></citation><citation key="ref26"><doi>10.1007/bfb0026683</doi><unstructured_citation>Joachims, T., 1998, April. Text categorization with support vector machines: Learning with many relevant features. In European conference on machine learning (pp. 137-142). Springer, Berlin, Heidelberg. </unstructured_citation></citation><citation key="ref27"><unstructured_citation>Maldonado, S. and L’Huillier, G., 2013. SVM-based feature selection and classification for email filtering. In Pattern recognitionapplications and methods (pp. 135-148). Springer, Berlin, Heidelberg. </unstructured_citation></citation><citation key="ref28"><unstructured_citation>Karypis, M.S.G., Kumar, V. and Steinbach, M., 2000, August. A comparison of document clustering techniques. In TextMining Workshop at KDD2000 (May 2000). </unstructured_citation></citation><citation key="ref29"><doi>10.1016/j.watres.2017.04.078</doi><unstructured_citation>Mujtaba, G. and Lee, K., 2017. Treatment of real wastewater using co-culture of immobilized Chlorella vulgaris and suspended activated sludge. Water research, 120, pp.174- 184. </unstructured_citation></citation><citation key="ref30"><unstructured_citation>Yang, Y. and Liu, X., 1999, August. A reexamination of text categorization methods. In Sigir (Vol. 99, No. 8, p. 99). </unstructured_citation></citation><citation key="ref31"><unstructured_citation>McCallum, A. and Nigam, K., 1998, July. A comparison of event models for naive bayes text classification. In AAAI-98 workshop on learning for text categorization (Vol. 752, No. 1, pp. 41-48). </unstructured_citation></citation><citation key="ref32"><unstructured_citation>Joachims, T., 1999. Svmlight: Support vector machine. SVM-Light Support Vector Machine http://svmlight. joachims. org/, University of Dortmund, 19(4). </unstructured_citation></citation><citation key="ref33"><unstructured_citation>Al-Gasawneh, J. A., &amp; Al-Adamat, A. M. (2020). THE RELATIONSHIP BETWEEN PERCEIVED DESTINATION IMAGE, SOCIAL MEDIA INTERACTION AND TRAVEL INTENTIONS RELATING TO NEOM CITY. Academy of Strategic Management Journal, 19(2). </unstructured_citation></citation><citation key="ref34"><doi>10.1109/cse-euc-dcabes.2016.238</doi><unstructured_citation>Dagher, Issam, and Rima Antoun. "Hamspam filtering using different PCA scenarios." In 2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES), pp. 542-545. IEEE, 2016.</unstructured_citation></citation></citation_list></journal_article></journal></body></doi_batch>