首页|New Machine Learning Study Findings Have Been Reported by Researchers at Royal M elbourne Institute of Technology - RMIT University (Enhancing Telemarketing Succ ess Using Ensemble-Based Online Machine Learning)
New Machine Learning Study Findings Have Been Reported by Researchers at Royal M elbourne Institute of Technology - RMIT University (Enhancing Telemarketing Succ ess Using Ensemble-Based Online Machine Learning)
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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.”
Royal Melbourne Institute of Technology - RMIT UniversityMelbourneAustraliaAustralia and New ZealandCyborgsEme rging TechnologiesMachine Learning
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