Abstract
The use of data mining techniques in the field of auctions has attracted considerable interest from the research community. In auctions, the users try to achieve the highest gain and avoid loss as much as possible. Therefore, data mining techniques can be implemented in the auctioning domain to develop an intelligent method that can be used by the users in online auctions. However, determining the factors that affect the result of an auction, especially the initial price, is critical. In addition, the intelligent system must be established based on clean data to ensure the accuracy of the results. In this paper, we propose an intelligent system (classifier) to predict the initial price of auctions. The proposed system uses the double smoothing method (DSM) for data cleaning in terms of preprocessing. This system is implemented on a data set collected from the eBay website and cleaned using the proposed DSM. In the training phase, the CART technique is employed for the classifier construction. Compared to similar techniques, the proposed system exhibits a better performance in terms of the accuracy and robustness against noisy data, as determined using ROC curves.
Highlights
Importance of the eBay website and auctions
The initial price plays a vital role in the decision making process conducted by the user for being involved or not in an auction
Preprocessing the data used in the training phase for the model construction is critical to ensure accurate results
Summary
Importance of the eBay website and auctions. The eBay website is one of the most important websites in the business area, and it can be considered as a leader among e-commerce and internet sites. eBay is used to sell and buy goods and products online or provide services worldwide. By using an intelligent machine that takes some of the influencing factors as inputs, businesspeople can predict the price premium In this context, the contributions of this work are as follows:. CART is a type of decision tree, which acts as a classifier, and it can be used in the classification process to address categorical data as a final decision (in this work, reaching or not reaching the price premium). This technique can be used for regression to deal with continuous data [3].
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More From: International Journal of Advanced Computer Science and Applications
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