Ace: a General-Purpose Classification Ensemble Optimization Framework

This paper describes ACE, a framework for automatically finding effective classification methodologies for arbitrary supervised classification problems. ACE performs experiments with both individual classifiers and classifier ensembles in order to find the approaches best suited to particular problems. A special emphasis is placed on classifier ensembles, as they can be powerful tools, yet are currently rarely used in MIR research. In addition to evaluating various classification methodologies in terms of success rates, ACE also allows users to specify constraints on training and classification times. The input to ACE is an arbitrary taxonomy accompanied by training feature vectors and their model classifications. ACE then outputs comparisons of the effectiveness of different classification methodologies, including information relating to feature weightings, dimensionality reduction and classifier combination techniques. The user may then select any of these configurations, after which s/he will be presented with trained classifiers that can be used to classify new feature vectors. Although designed to be used easily with any existing feature extraction system, ACE is also packaged with MIDI and audio feature extraction sub-systems. In addition, plans are underway to make use of distributed computing in order to decrease processing times.