This week I listened to Jenna's presentation of 'Multi-Label Classification: An Overview'. It was an evaluation of different classification methods to compute the best accuracy of each multi-label by using problem transformation methods. Three problem transformation methods were implemented in conjunction with the algorithms kNN, Naive Bayes and an addition of Support Vector Machine (SMO). The dataset focused on Genbase and Scene, and the best results were given when each set of labels was considered a single label and used the SMO algorithm. This performance achieved the highest mean accuracy for all of the learning algorithms used within each data set.
On Saturday, Jenna will be giving a talk on Spark and data analysis with Tweet data. There, I will gain further insight on Apache Spark and learning Scala. I've been experimenting with test data on my machine, and I hope to gain more experience with machine learning algorithms.
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