Machine Learning for Survival Prediction in Breast Cancer

In the last few years, machine learning revealed an important instrument to support decision making in oncology. In this manuscript, an application is presented about the use of several machine learning algorithms for the prediction of the survival rate of breast cancer patients. Before presenting the results, the manuscript contains a rather basic introduction to the foundations of machine learning, that can be useful for medical doctors that are not expert in the area. The experiments were carried on using the well-known 70-gene signature dataset for breast cancer. The presented results highlight that genetic programming has interesting advantages compared to other machine learning algorithms, both in terms of prediction accuracy and in terms of model interpretability.info:eu-repo/semantics/publishedVersio

Understanding over-indebtedness in Portugal: descriptive and predictive models.

Over-indebtedness is a recurring problem in Portugal. After facing different economic cycles, between financial crises and prosperity periods, Portuguese consumers have been striving to keep their household finances stable and avoid being over-indebted. This project aims to gain insights on over-indebtedness, from different perspectives that range from the social to the economic point of view. It examines over-indebtedness from a psychological and from a data science perspective. In particular, we suggest that the systemic impact of financial crisis in Portugal not only promotes over-indebtedness, but it crafts a specific profile of over-indebted consumers which may be distinguished from other profiles, ranging from the emphasis on lack of self-regulation and careless management of one’s budget to other causal factors such as consumerism, crisis, and unemployment. Given this scenario, this project proposes the use of Machine Learning (ML) for developing descriptive and predictive models, to understand the influencing factors of over-indebtedness on Portuguese consumers and will be used for establishing consumer clusters and guidelines for over-indebtedness regulation and consumer financial empowerment.info:eu-repo/semantics/publishedVersio

Genetic programming with semantic equivalence classes

Ruberto, S., Vanneschi, L., & Castelli, M. (2019). Genetic programming with semantic equivalence classes. Swarm and Evolutionary Computation, 44(February), 453-469. DOI: 10.1016/j.swevo.2018.06.001In this paper, we introduce the concept of semantics-based equivalence classes for symbolic regression problems in genetic programming. The idea is implemented by means of two different genetic programming systems, in which two different definitions of equivalence are used. In both systems, whenever a solution in an equivalence class is found, it is possible to generate any other solution in that equivalence class analytically. As such, these two systems allow us to shift the objective of genetic programming: instead of finding a globally optimal solution, the objective is now to find any solution that belongs to the same equivalence class as a global optimum. Further, we propose improvements to these genetic programming systems in which, once a solution that belongs to a particular equivalence class is generated, no other solution in that class is accepted in the population during the evolution anymore. We call these improved versions filtered systems. Experimental results obtained via seven complex real-life test problems show that using equivalence classes is a promising idea and that filters are generally helpful for improving the systems' performance. Furthermore, the proposed methods produce individuals with a much smaller size with respect to geometric semantic genetic programming. Finally, we show that filters are also useful to improve the performance of a state-of-the-art method, not explicitly based on semantic equivalence classes, like linear scaling.authorsversionpublishe