Each year, Risk Quant Europe Conference, a conference well-attended by practitioners from banking, asset management, insurers as well as academics from Europe, selects two papers to present in their annual conference.
For 2018, our paper is lucky to be one of the two winning papers selected by the Advisory Board for the conference to be held in London. Please feel free to check out our paper titled CDS Rate Construction Methods by Machine Learning…Continue
Added by Zhongmin Luo on February 24, 2018 at 2:00am — No Comments
Summary: Dealing with imbalanced datasets is an everyday problem. SMOTE, Synthetic Minority Oversampling TEchnique and its variants are techniques for solving this problem through oversampling that have recently become a very popular way to improve model performance.
There are some problems that never go away. …Continue
Does it sound familiar to you? In order to get an idea of how to choose a parameter for a given classifier, you have to cross reference to a number of papers or books, which often turn out to present competing arguments for or against a certain parameterization choice but with few applications to real-world problems.
For example, you may find a few papers discussing optimal selection of K in…Continue
In practice, we often have to make parameterization choices for a given classifier in order to achieve optimal classification performances; just to name a few examples:
Added by Zhongmin Luo on May 29, 2017 at 12:49am — No Comments
Past literature show that the comparisons of classifier's performance are specific to the types of datasets (e.g., Pharmaceutical industry data) used; i.e., some classifiers may perform better in some context than others. A paper titled CDS Rate Construction Methods by Machine Learning Techniques conducts the performance comparison exclusively in the context of financial market by applying a wide range of classifiers to provide solution to so-called Shortage of…Continue
Added by Zhongmin Luo on May 23, 2017 at 1:30am — No Comments
Many Machine Learning articles and papers describe the wonders of the Support Vector Machine (SVM) algorithm. Nevertheless, when using it on real data trying to obtain a high accuracy classification, I stumbled upon several issues.
I will try to describe the steps I took to make the algorithm work in practice.
This model was implemented…
Added by Renata Ghisloti Duarte Souza Gra on December 18, 2015 at 5:00pm — No Comments