Experimental Comparisons of Multi-class Classifiers - Li, Lin; Wu, Yue; Ye, Mao (Informatica, 2015)
The multi-class classification algorithms are widely used by many areas such as machine learning andcomputer vision domains. Nowadays, many literatures described multi-class algorithms, however thereare few literature that introduced them with thorough theoretical analysis and experimentalcomparisons. This paper discusses the principles, important parameters, application domain, runtimeperformance, accuracy, and etc. of twelve multi-class algorithms: decision tree, random forests,extremely randomized trees, multi-class adaboost classifier, stochastic gradient boosting, linear andnonlinear support vector machines, K nearest neighbors, multi-class logistic classifier, multi-layerperceptron and naive Bayesian classifier. The experiment tested on five data sets: SPECTF heart dataset, Ionosphere radar data set, spam junk mail filter data set, optdigits handwriting data set and scene15 image classification data set. Our major contribution is that we study the relationships between eachclassifier and impact of each parameters to classification results. The experiment shows that gradientboosted trees, nonlinear support vector machine, K nearest neighbor reach high performance under thecircumstance of binary classification and minor data capacity; Under the condition of high dimension,multi-class and big data, however, gradient boosted trees, linear support vector machine, multi-classlogistic classifier get good results. At last, the paper addresses the development and future of multi-classclassifier algorithms.