Discriminative vs. Generative Classifiers : An In-Depth Experimental Comparison using Cost Curves
نویسنده
چکیده
Permission is granted to quote short excerpts and to reproduce figures and tables from this report, provided that the source of such material is fully acknowledged. Permission is granted to quote short excerpts and to reproduce figures and tables from this report, provided that the source of such material is fully acknowledged. Abstract This technical report discusses the experimental comparison of commonly used algorithms both in their traditional discriminative form and as generative classifiers. The performance is compared using cost curves to see what benefits might be gained by using a generative classifier when the misclassification costs, and class frequencies, are unknown. There is some evidence that learning a discriminative classifier is more effective for a traditional classification task. Focusing on algorithms that have gener-ative and discriminative forms, allows a clear comparison between these two types of classifier without being obscured by algorithmic differences. The report compares the performance of the classifiers over 16 data sets and for the full range of misclassification costs and class frequencies. The experiments show that there is some merit in using generative classifiers for cost sensitive learning but more work is needed to make them as effective as using multiple discriminative classifiers.
منابع مشابه
Discriminative vs. Generative Classifiers for Cost Sensitive Learning
This paper experimentally compares the performance of discriminative and generative classifiers for cost sensitive learning. There is some evidence that learning a discriminative classifier is more effective for a traditional classification task. This paper explores the advantages, and disadvantages, of using a generative classifier when the misclassification costs, and class frequencies, are n...
متن کاملOn Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes
We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widelyheld belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size is increased, one in which each algorithm does better. This stems from the observationwhich is borne o...
متن کاملClassification with Hybrid Generative/Discriminative Models
Although discriminatively-trained classifiers are usually more accurate when labeled training data is abundant, previous work has shown that when training data is limited, generative classifiers can out-perform them. This paper describes a hybrid model in which a high-dimensional subset of the parameters are trained to maximize generative likelihood, and another, small, subset of parameters are...
متن کاملEfficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers
We introduce a simple order-based greedy heuristic for learning discriminative structure within generative Bayesian network classifiers. We propose two methods for establishing an order of N features. They are based on the conditional mutual information and classification rate (i.e., risk), respectively. Given an ordering, we can find a discriminative structure with O ( Nk+1 ) score evaluations...
متن کاملThe Trade-off between Generative and Discriminative Classifiers
Given any generative classifier based on an inexact density model, we can define a discriminative counterpart that reduces its asymptotic error rate. We introduce a family of classifiers that interpolate the two approaches, thus providing a new way to compare them and giving an estimation procedure whose classification performance is well balanced between the bias of generative classifiers and ...
متن کامل