Besides obvious classifier characteristics like
- computational cost,
- expected data types of features/labels and
- suitability for certain sizes and dimensions of data sets,
what are the top five (or 10, 20?) classifiers to try first on a new data set one does not know much about yet (e.g. semantics and correlation of individual features)? Usually I try Naive Bayes, Nearest Neighbor, Decision Tree and SVM - though I have no good reason for this selection other than I know them and mostly understand how they work.
I guess one should choose classifiers which cover the most important general classification approaches. Which selection would you recommend, according to that criterion or for any other reason?
UPDATE: An alternative formulation for this question could be: "Which general approaches to classification exist and which specific methods cover the most important/popular/promising ones?"