In data stream classification, selecting the classifier for the dynamic feature space and considering the concept drift is a challenging task. This paper addresses the major challenges in the data stream classification with recurring concept drift. We developed a novel classification method known as Pearson Guassian Naïve Bayes classification (PGNBC). The proposed PGNBC method is the advancement over the existing Guassian Naïve Bayes classifier (GNBC) by additionally adding the correlation among the attributes. For the data stream classification, the proposed PGNBC is frequently updated based on the concept drift. This newly developed method is experimented by comparing the results with the existing methods such as RGNBC and MReC-DFS. The metrics such as sensitivity, specificity and accuracy are used for measuring the performance. It is found that the improvement in terms of sensitivity, specificity and accuracy values are better for the proposed method, with the values of 4%, 1% and 1% respectively, which is higher for the PGNBC method than the RGNBC method for the skin data. But with the localization data, the improvement in terms of specificity and accuracy values are 6% and 2% respectively which is higher than the RGNBC.
Mena-TorresD. and Aguilar-RuizJ.S., A similarity-based approach for data stream classification, Expert Systems with Applications41 (2014), 4224–4234.
2.
AlippiC.LiuD.ZhaoD. and BuL., Detecting and Reacting to Changes in Sensing Units: The Active Classifier Case, IEEE Transactions on Systems, Man, and Cybernetics: Systems44(3) (2013), 353–362.
3.
ZhangP.ZhouC.WangP.GaoB.J.ZhuX. and GuoL., E-Tree: An Efficient Indexing Structure for Ensemble Models on Data Streams, IEEE Transactions on Knowledge and Data Engineering27(2) (February 2015), 461–474.
4.
RutkowskiL.JaworskiM.PietruczukL. and DudaP., Decision Trees for Mining Data Streams Based on the Gaussian Approximation, IEEE Transactions on Knowledge and Data Engineering26(1) (January 2014), 108–119.
5.
BrzezinskiD. and StefanowskiJ., Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm, IEEE Transactions on Neural Networks and Learning Systems25(1) (January 2014), 81–94.
6.
GomesJ.B.GaberM.M.SousaP.A.C. and MenasalvasE., Mining Recurring Concepts in a Dynamic Feature Space, IEEE Transactions on Neural Networks and Learning Systems25(1) (January 2014), 95–110.
7.
MasudM.M.ChenQ.KhanL.AggarwalC.C.GaoJ.HanJ.SrivastavaA. and OzaN.C., Classification and Adaptive Novel Class Detection of Feature-Evolving Data Streams, IEEE Transactions on Knowledge and Data Engineering25(7) (July 2013), 1484–1497.
8.
AbdulsalamH.SkillicornD.B. and MartinP., Classification Using Streaming Random Forests, IEEE Transactions on Knowledge and Data Engineering23(1) (January 2011), 22–36.
9.
FanW., Systematic Data Selection to Mine Concept-Drifting Data Streams, in: Proc. ACM SIGKDD 10th Int’l Conf. Knowledge Discovery and Data Mining, 2004, pp. 128–137.
10.
GaoJ.FanW. and HanJ., On Appropriate Assumptions to Mine Data Streams, in: Proc. IEEE Seventh Int’l Conf. Data Mining (ICDM), 2007, pp. 143–152.
11.
HultenG.SpencerL. and DomingosP., Mining Time-Changing Data Streams, in: Proc. ACM SIGKDD Seventh Int’l Conf. Knowledge Discovery and Data Mining, 2001, pp. 97–106.
12.
KolterJ. and MaloofM., Using Additive Expert Ensembles to Cope with Concept Drift, in: Proc. 22nd Int’l Conf. Machine Learning (ICML), 2005, pp. 449–456.
13.
WangH.FanW.YuP.S. and HanJ., Mining Concept-Drifting Data Streams Using Ensemble Classifiers, in: Proc. ACM SIGKDD Ninth Int’l Conf. Knowledge Discovery and Data Mining, 2003, pp. 226–235.
14.
GomesJ.B.MenasalvasE. and SousaP., Tracking recurrent concepts using context, in: Proc. 7th Int. Conf. RSCTC, 2010, pp. 168–177.
15.
GamaJ. and KosinaP., Tracking recurring concepts with metalearners, in: Proc. 14th Portuguese Conf. Artif. Intell., Oct. 2009, pp. 423.
16.
KatakisI.TsoumakasG. and VlahavasI., On the utility of incremental feature selection for the classification of textual data streams, in Advances in Informatics. New York, NY, USA: Springer-Verlag, 2005, 338–348.
17.
YangY.WuX. and ZhuX., Mining in anticipation for concept change: Proactive-reactive prediction in data streams, Data Mining Knowl. Discovery13(3) (2006), 261–289.
18.
ZhouY.HowroydJ.DanicicS. and BishopJ.M., Extending Naive Bayes Classifier with Hierarchy Feature Level Information for Record Linkage, Advanced Methodologies for Bayesian Networks9505 (2015), 93–104.
19.
LiJ.FangG.LiB. and WangC., A Novel Naive Bayes Classifier Model Based on Differential Evolution, Intelligent Computing Theories and Methodologies9225 (August 2015), 558–566.
20.
LeeC.H., A gradient approach for value weighted classification learning in naive Bayes, Knowledge-Based Systems85 (September 2015), 71–79.
21.
KarabatakM., A new classifier for breast cancer detection based on Naïve Bayesian, Measurement72 (August 2015), 32–36.
22.
UC Irvine Machine Learning Repository from http://archive.ics.uci.edu/ml/datasets.html.
23.
PinheiroR.H.W.CavalcantiG.D.C. and IngRenT., Data-driven global-ranking local feature selection methods for text 4 categorization, Expert Systems with Applications42(4) (March 2015), 1941–1949.
24.
RishI., An empirical study of the naive Bayes classifier, in proceedinsg of IJCAI Workshop on Empirical Methods in AI, 2001.
25.
PawlakZ., Rough sets, International Journal of Parallel Programming11(5) (1982), 341–356.
26.
WankhadeK.RaneD. and ThoolR., A new feature selection algorithm for stream Data Classification, in: Proceedinsg of International Conference on Advances in Computing, Communications and Informatics (ICACCI), August 2013.
27.
LutuP.E.N., Fast Feature Selection for Naive Bayes Classification in Data Stream Mining, Proceedings of the World Congress on Engineering 2013 Vol III, WCE 2013, July 3–5, 2013.
28.
YuL. and LiuH., Feature selection for high-dimensional data: A fast correlation-based filter solution, in: Proceedings of the International Conference on Machine Leaning, 2003, pp. 856–863.
29.
BermejoP.GámezJ. and PuertaJ., Speeding up incremental wrapper feature subset selection with Naive Bayes classifier, Knowledge-Based Systems55 (2014), 140–147.
