摘要
由一名新闻记者兼机器人与机器学习每日新闻编辑每日新闻-关于人工智能ce的详细数据已经呈现。根据NewsRx编辑在澳大利亚墨尔本的新闻报道，研究表明，“电话营销是向潜在客户提供产品和服务的一种成熟的市场营销方法。然而，这种方法的有效性在很大程度上取决于选择合适的消费者基础，因为接触到不感兴趣的客户将会导致损失，浪费昂贵的企业资源，而忽略感兴趣的客户。”新闻编辑们从皇家墨尔本理工大学墨尔本分校的研究中引用了一句话:“商业智能和机器学习模型的引入可以通过预测潜在的客户群对决策过程产生积极的影响,这方面的现有文献显示出了良好的结果。然而,商业智能和机器学习模型的引入可以通过预测潜在的客户群而对决策过程产生积极的影响。”选择有影响力的特征并构建有效的学习模型，以提高挑战中的绩效。此外，从建模的角度来看，培训数据的类别不平衡性质，即结果不成功的样本与成功的样本之间的比例很高，通过创建有偏见的精确模型，进一步加剧了问题。此外，由于各种原因，客户偏好可能会随着时间的推移而改变。和/或新的客户群体可能会成为新产品或服务的目标，因此需要对现有工作中根本没有涉及的模型进行再培训。模型再培训的一个主要挑战是在稳定性（保留旧知识）和可塑性（对新信息的感知）之间保持平衡。为了解决上述问题，为了更准确地识别潜在客户,提出了一种基于特征选择和过采样技术的集成机器学习模型,并提出了一种新的在线学习方法,用于新样本随时间可用时的模型再培训。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news reporting out of Melbourne, Australia, by NewsRx editors, research stated, “Telemarketing is a well-established market ing approach to offering products and services to prospective customers. The eff ectiveness of such an approach, however, is highly dependent on the selection of the appropriate consumer base, as reaching uninterested customers will induce a nnoyance and consume costly enterprise resources in vain while missing intereste d ones.” The news editors obtained a quote from the research from Royal Melbourne Institu te of Technology - RMIT University: “The introduction of business intelligence a nd machine learning models can positively influence the decision-making process by predicting the potential customer base, and the existing literature in this d irection shows promising results. However, the selection of influential features and the construction of effective learning models for improved performance rema in a challenge. Furthermore, from the modelling perspective, the class imbalance nature of the training data, where samples with unsuccessful outcomes highly ou tnumber successful ones, further compounds the problem by creating biased and in accurate models. Additionally, customer preferences are likely to change over ti me due to various reasons, and/or a fresh group of customers may be targeted for a new product or service, necessitating model retraining which is not addressed at all in existing works. A major challenge in model retraining is maintaining a balance between stability (retaining older knowledge) and plasticity (being re ceptive to new information). To address the above issues, this paper proposes an ensemble machine learning model with feature selection and oversampling techniq ues to identify potential customers more accurately. A novel online learning met hod is proposed for model retraining when new samples are available over time.”
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