Adaptive Detection of Shill Bidding and Multi-Objective Winner Determination

在电子拍卖中需要解决的两个重大研究挑战是：监测拍卖中的欺诈行为，以及确定高级拍卖的获胜者。由于用户的匿名性和涉及的金额巨大，电子拍卖对欺诈者非常有吸引力。发现电子拍卖中的欺诈具有挑战性，因为电子拍卖是一个庞大而活跃的市场。Shill Bidding（SB）是指有人出价人为提高物品价格的行为，由于其与通常的出价行为相似，因此是最难检测的拍卖欺诈行为。关于应用机器学习检测SB的研究有限，其中大多数都是离线进行的。自适应学习是必要的，以提高分类器的性能随着时间的推移，这是至关重要的欺诈检测问题。我们的目标是设计一个解决方案，解决SB检测的当前挑战。我们将开发一个高质量的标记，采样SB训练数据集，这是目前缺乏和不可用的;开发一个在线的自适应分类器，将随着新的投标趋势不断发展;并开发一种欺诈验证方法，因为训练数据在没有任何地面事实的情况下使用。我们的动机是有效地解决这些问题的基础上，国家的最先进的技术，数据聚类和标签，不平衡的数据采样，增量和减量分类，自动欺诈验证。学习的SB模型按需对从eBay抓取的新数据进行分类，eBay每天举行数千次电子拍卖。它将在投标期结束时启动，但在确定获胜者之前，以避免经济损失。我们的SB分类器将不断调整有信心的标记数据，这将提高其性能和训练数据的基础事实。组合逆向拍卖由于其资源配置效率而成为一种经济的采购方式。CRA允许卖方对单个买方所需的一系列商品或服务进行投标。我们的目标是开发一个强大的赢家确定（WD）的方法，先进的信用评级机构，目前受到交易的限制和相互冲突的目标。我们的问题本质上是目标最大化和最小化的混合。我们将正式的WD作为一个多目标优化（MOO）的问题，我们寻找一组折衷的解决方案，每一个优化冲突的目标同时。然而，执行这种权衡分析是计算密集型的。在拍卖设置中，执行时间是一个关键要求。为了解决这一挑战，我们将定义一个进化的基于MOO的WD方法。作为一个真实的案例研究，我们将量身定制新的WD方法，以优化采购电力从不同的能源，并评估其性能。此外，我们将进行性能分析的WD方法的基础上大规模的实例CRA。

Two significant research challenges to be addressed in electronic (e) auctions are: monitoring auctions for fraud, and determining the winners for advanced auctions. Due to the anonymity of users and large amounts of money involved, e-auctions are very attractive to fraudsters. Detecting fraud in e-auctions is challenging because they constitute a voluminous and dynamic market. Shill Bidding (SB), which occurs when someone places a bid to artificially increase the price of an item, is the hardest auction fraud to detect due to its similarity to usual bidding behavior. There are limited studies on applying machine learning to detect SB, and most of them have been conducted offline. Adaptive learning is necessary to improve a classifier's performance over time, which is crucial for fraud detection problems. Our goal is to devise a solution that addresses the current challenges of SB detection. We will develop a high-quality labelled, sampled SB training dataset, which is currently lacking and unavailable; develop an online, adaptive classifier that will evolve continuously with new bidding trends; and develop a fraud verification method as training data are used without any ground truth. We are motivated by solving these problems efficiently based on state-of-the-art techniques for data clustering and labeling, imbalanced data sampling, incremental and decremental classification, and automated fraud verification. The learned SB model classifies on demand new data crawled from eBay where thousands of e-auctions are held daily. It is to be launched at the end of the bidding period but prior to determining the winners to avert financial loss. Our SB classifier will be constantly adjusted with confidently labeled data, and this will improve its performance and the ground truth of training data.******Combinatorial Reverse Auctions (CRAs) represent an economical procurement method due to their allocative efficiency. CRAs allow sellers to bid on a bundle of goods or services required by a single buyer. Our aim is to develop a robust Winner Determination (WD) method for advanced CRAs, currently subject to trading constraints and conflicting objectives. Inherent to our problem is a mixture of maximization and minimization of objectives. We will formalize the WD as a Multi-Objective Optimization (MOO) problem for which we search a set of trade-off solutions; each one optimizes the conflicting objectives simultaneously. However, performing this trade-off analysis is computationally intensive. In an auction setting, the execution time is a critical requirement. To address this challenge, we will define an evolutionary MOO-based WD method. As a real case study, we will tailor the new WD method to the electricity market to optimally procure power from different energy sources, and assess its performance. Moreover, we will conduct a performance analysis of the WD method based on large-scale instances of CRAs.