Adaptive Detection of Shill Bidding and Multi-Objective Winner Determination

电子拍卖中需要解决的两个重要研究挑战是：监控拍卖中的欺诈行为，以及确定高级拍卖的获胜者。由于用户的匿名性和涉及的巨额资金，电子拍卖对诈骗者非常有吸引力。检测电子拍卖中的欺诈行为具有挑战性，因为它们构成了一个庞大而充满活力的市场。Shill Bidding （SB）指的是某人通过出价来人为提高某件物品的价格，这是最难发现的拍卖欺诈行为，因为它与通常的竞标行为相似。应用机器学习检测SB的研究有限，而且大多数都是离线进行的。随着时间的推移，自适应学习是提高分类器性能的必要条件，这对欺诈检测问题至关重要。我们的目标是设计一个解决方案，以解决当前SB检测的挑战。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、自适应的分类器，它将随着新的竞标趋势不断发展；并开发了一种欺诈验证方法，因为使用的训练数据没有任何基础事实。我们的动机是基于最先进的数据聚类和标记、不平衡数据采样、增量和递减分类以及自动欺诈验证等技术，有效地解决这些问题。学习后的SB模型根据需要对从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分类器将不断调整自信标记的数据，这将提高其性能和训练数据的真实度。******组合逆向拍卖（CRAs）是一种经济的采购方式，由于其分配效率。信用评级机构允许卖方对单个买方需要的一捆商品或服务进行投标。我们的目标是为目前受到交易限制和目标冲突的高级评级机构开发一个强大的赢家确定（WD）方法。我们的问题的本质是目标的最大化和最小化的混合。我们将WD形式化为一个多目标优化（MOO）问题，为此我们搜索一组权衡解决方案；每个人同时优化冲突的目标。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。为了应对这一挑战，我们将定义一种进化的基于moo的WD方法。作为一个实际的案例研究，我们将根据电力市场调整新的WD方法，以最优地从不同的能源中获取电力，并评估其性能。此外，我们将对基于大规模cra实例的WD方法进行性能分析。

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.