There is a growing body of litterature regarding the use of Machine Learning Techniques to build a classifier. How can one come up with such a classifier in order to build a contry-wide prediction model of insecticide resistance in malaria vectors?
It is often easier to build a classifier than to find the data for classification. And data must contain the information about the problem, although in a hidden or a twisted way.
If you have a data about country-wise insecticide resistance, I would recommend starting from a general classifier, like Support Vector Machines (SVM) or Neural Networks. Look for a toolbox in the programming language of your choice (MATLAB, Python, etc.)
In my opinion, you'd better obtain the dataset of country-wise insecticide resistance. Then you may try several popular techniques (Support Vector Machines (SVM), Neural Networks, Decision Tree, Bayesian Network...) to identify the most suitable one.
Thank you all for your answers. My colleagues and I have a huge dataset on vector resistance to different insecticides. Can anyone recommend a documentation to better learn the tools that you both mentioned?
An excellent book is entitled "Pattern Classification" by Duda, Hart, and Stork, 2nd edition, 2000. It is one of the best textbooks in the field.
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