Outlier Detection Based on Majority Voting: A Case Study on Real Estate Prices

Abstract

Outliers in the data are very common for various fields. So filtering the data is prominent both for computing the desired result for a data set correctly or noticing unusual behaviours. In this case study, outlier detection is used to detect false ads, which are placed in the wrong category or have the wrong values, in a real estate sale website. To accomplish this, two websites are crawled, and the real estates with the unexpectedly low or high price per meter-square value are considered as the outlier candidates. To detect outliers, live outlier detection algorithms arc run separately and majority voting is used to determine the absolute result, the average price per meter-square in the location. Evaluating the results of algorithms by majority voting, enabled to tolerate deficiencies of an algorithm by others automatically with some other benefits as well.