Abstract
Detecting outliers is an important step in data mining. Outliers not only hamper data quality but also affect the output in case of prediction models. Prediction tools like Neural Networks (NN) need outlier free dataset in order to achieve better generalization of the network as errors in the dataset hinder the modelling process and produce misleading results. Thus, range of the dataset needs to be curbed in order to make it fit for generating better prediction results. However, outlier detection faces one difficulty. There is no standard framework for the treatment of outliers found in the literature. The present study is an effort to identify the most suited outlier detection method for a specific problem, which deals with the use of NN for prediction of real property value. 3094 cases of property sale instances are presented to various univariate outlier detection methods like Tukey’s method, Standard Deviation (SD) method, median method, Z score method, MAD method, modified Z score method, etc. The datasets prepared after removing outliers marked for respective methods are used for prediction using NN. Comparison of results show that the median method is the best-suited outlier detection method for the present study.
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References
R. Johnson, D.W. Wichern, Applied Multivariate Statistical Analysis, 6th edn. (Prentice Hall, Englewood Cliffs, 1992)
V. Chandola, A. Banerjee, V. Kumar, Anomaly detection: a survey. ACM Comput. Surv. 41(3), Article 15 (2009)
V. Barnett, T. Lewis, Outliers in Statistical Data, 3rd edn. (Wiley, New York, 1994). (ISBN 978-0-471-93094-5)
I. Ben Gal, Outlier detection, in Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, ed. by O. Maimon, L. Rockach (Kluwer Academic Publishers, Dordrecht, 2005)
R.J. Bullen, D. Cornforda, I.T. Nabneya, Outlier detection in scatterometer data: neural network approaches. Neural Netw. Spec. Issue 16, 419–426 (2003)
G.J. Williams, R.A. Baxter, H.X. He, S. Hawkins, L. Gu, A comparative study of RNN for outlier detection in data mining, in IEEE International Conference on Data-Mining (ICDM’02), Maebashi City, Japan, CSIRO Technical Report CMIS-02/102 (2002)
H. Liu, S. Shah, W. Jiang, On-line outlier detection and data cleaning. Comput. Chem. Eng. 28, 1635–1647 (2004)
S. Seo, A review and comparison of methods for detecting outliers in univariate data sets, Dissertation, Graduate School of Public Health, Kyunghee University, University of Pittsburgh (2006). Accessed at: http://d-scholarship.pitt.edu/7948/. Accessed on 20 Apr 2017
D. Cousineau, S. Chartier, Outliers detection and treatment: a review. Int. J. Psychol. Res. 3(1), 58–67 (2010)
N. Upasania, H. Omb, Evolving fuzzy min-max neural network for outlier detection, in International Conference on Advanced Computing Technologies and Applications (ICACTA-2015) Proceedia Computer Science, vol. 45, pp. 753–761 (2015)
A.M. Rajeswari, M. Sridevi, C. Deisy, Outliers detection on educational data using fuzzy association rule mining, in International Conference on Advance in Computer Communication and Information Science (ACCIS-14), pp. 1–9 (2014)
M. Nkurunziza, L. Vermeire, A comparison of outlier labelling criteria in univariate measurements, Sustainability in statistics education, in Proceedings of the Ninth International Conference on Teaching Statistics (ICOTS9, July, 2014), Flagstaff, Arizona, USA (International Statistical Institute, Voorburg, The Netherlands), pp. 1–4 (2014)
M. Markou, S. Singh, Novelty detection: a review-part 1: statistical approaches. Sig. Process. 83(12), 2481–2497 (2003)
M. Markou, S. Singh, Novelty detection: a review-part 2: neural network based approaches. Sig. Process. 83(12), 2499–2521 (2003)
V. Hodge, J. Austin, A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85–126 (2004)
M. Agyemang, K. Barker, R. Alhajj, A comprehensive survey of numeric and symbolic outlier mining techniques. Intell. Data Anal. 10(6), 521–538 (2006)
A. Patcha, J.M. Park, An overview of anomaly detection techniques: existing solutions and latest technological trends. Comput. Netw. 51(12), 3448–3470 (2007)
P. Gogoi, D.K. Bhattacharyya, B. Borah, J.K. Kalita, A survey of outlier detection methods in network anomaly identification. Comput. J. 54(4), 570–588 (2011)
M. Daszykowski, K. Kaczmarek, Y. Vander Heyden, B. Walczak, Robust statistics in data analysis-a review basic concepts. Chemometr. Intell. Lab. Syst. 85, 203–219 (2007)
J.W. Tukey, Exploratory Data Analysis, 1st edn. (Addison-Wesely Publishers, Reading, MA, 1977). (ISBN-13: 978-0201076165)
S.S. Tripathy, R.K. Saxena, P.K. Gupta, Comparison of statistical methods for outlier detection in proficiency testing data on analysis of lead in aqueous solution. Am. J. Theor. Appl. Stat. 2(6), 233–242 (2013)
A. Gautam, V. Bhateja, A. Tiwari, S.C. Satapathy, An improved mammogram classification approach using back propagation neural network, in Data Engineering and Intelligent Computing (Springer, Singapore, 2018), pp. 369–376
S. Sandbhor, N.B. Chaphalkar, Effect of training sample and network characteristics in neural network based real property value prediction, Proceedings of 2nd International Conference on Data Engineering and Communication Technology (ICDECT) 2017, 15th–16th December 2017, ‘Advances in Intelligent Systems and Computing (AISC) Series of Springer’ (SCOPUS), 303–313 (2018)
D. Tay, D. Ho, Artificial intelligence and the mass appraisal of residential apartments. J. Prop. Valuat. Invest. 10, 525–539 (1991)
A. Do, G. Grudnitski, A neural network approach to residential property appraisal. R. Estate Apprais. 58, 38–45 (1992)
N. Nghiep, Al Cripps, Predicting housing value: a comparison of multiple regression analysis and artificial neural networks. J. R. Estate Res. 22(3), 313–336 (2001)
A. Khamis, Z. Ismail, K. Haron, A. Muhammed, The effects of outliers data on neural network performance. J. Appl. Sci. 5(8), 1394–1398 (2005)
S. Sandbhor, N.B. Chaphalkar, Determining attributes of Indian real property valuation using principal component analysis. J. Eng. Technol. 6(2), 483–495 (2017)
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Sandbhor, S., Chaphalkar, N.B. (2019). Impact of Outlier Detection on Neural Networks Based Property Value Prediction. In: Satapathy, S., Bhateja, V., Somanah, R., Yang, XS., Senkerik, R. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 862. Springer, Singapore. https://doi.org/10.1007/978-981-13-3329-3_45
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