PORTFOLIO OPTIMIZATION USING MULTI-CRITERIA DECISION ANALYSIS AND MACHINE LEARNING

金融投资问题的一个主要特征是，情况经常随着时间的推移而变化。在这种情况下应用机器学习技术，特别是，已经观察到额外的学习起着有效的作用。然而，由于分类规则变得越来越复杂，只有额外的学习，一些适当的遗忘也是必要的。随着时间的流逝，许多数据被遗忘似乎是很自然的。我们把这种仅仅基于时间流逝的遗忘方式称为“被动遗忘”。另一方面，希望更有效地忘记不必要的数据。我们把这种主动遗忘的方式称为“不必要的数据”。在本研究中，我们发展了几种机器学习中的主动遗忘方法，并将其应用于股票投资组合问题。因此，已经表明主动遗忘比单纯的额外学习或被动遗忘提供更好的结果。可以是exp ...更多信息 认为可以通过应用一些多目标规划技术（例如，在第一年的研究中，我们发展了RBF网络的附加学习和被动遗忘。通过数值实验，证明了该方法在股票投资组合问题中的有效性。尽管许多机器学习技术（诸如人工神经网络）可以提供良好的结果，但是它们不是透明的（即，黑盒子）。在许多实际情况下，人们想知道预测是如何做出的。为此，需要提取显式规则。在本研究的最后一年，作者提出了主动遗忘的概念。将主动遗忘应用于势函数法，在股票投资组合问题中取得了显著的效果。在此基础上，尝试将上述机器学习技术与多目标规划技术联合收割机相结合，建立了一个股票投资组合决策支持系统。少

One of main features in financial investment problems is that the situation changes very often over time. In applying machine learning techniques under this circumstance, in particular, it has been observed that additional learning plays an effective role. However, since the rule for classification becomes more and more complex with only additional learning, some appropriate forgetting is also necessary. It seems natural that many data are forgotten as the time elapses. We call the way of forgetting based only on the time elapse "passive forgetting". On the other hand, it is expected more effective to forget unnecessary data actively. We call this way of forgetting "unnecessary data" actively "active forgetting". In this research, several ways for active forgetting in machine leaning have been developed and applied to stock portfolio problems. As a result, it has been shown that active forgetting provides better results than mere additional learning or passive forgetting. It can be exp … More ected that an effective decision support system for portfolio problems can be obtained by applying some of multi-objective programming techniques (e.g., Satisficing Trade-off Method developed by the author) to candidate stocks which are selected by machine learning with active forgetting.In the first year of the research term, additional learning and passive forgetting in RBF networks was developed. Through numerical experiments, it was shown that this new technology works effectively in stock portfolio problems.In the next year of the research term, rule extraction was tried by using the rough set theory. Although many machine learning techniques such as artificial neural networks can provide good results, they are not transparent (i.e., of black box). In many actual situations, people want to see how the prediction was made. To this end, extraction of explicit rules is needed. It was shown that the rough set theory can work effectively for this purpose.In the last year of the research term, active forgetting was developed. Applying active forgetting in the potential method, remarkably beneficial results were obtained in stock portfolio problems. On the basis of the obtained results, a decision support system for stock portfolio is on trial to combine the above machine learning techniques and multi-objective programming techniques. Less