I’m taking a machine learning class and I have a project, whose task is to implement an original supervised learning algorithm. It doesn’t have to be something new from scratch and it doesn’t need to be overcomplicated, because it’s a one week project. It can be a combination of two learning algorithms that can accurately classify a labeled data set, such as using genetic algorithms with artificial neural networks. It can also use part of an existing algorithm, as long as I add something substantial to it. The problem is I can’t think of a simple idea that is not already proposed in a published research paper.

To put things into perspective, the learning algorithms I’m familiar with are:

• Decision Trees
• KNN
• ANN
• SVM
• Linear and Logistic Regression
• Genetic Algorithm
• Clustering