Discriminative vs. Generative Classifiers for Cost Sensitive Learning
نویسنده
چکیده
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 not fixed. The paper details experiments built around commonly used algorithms modified to be cost sensitive. This allows a clear comparison to the same algorithm used to produce a discriminative classifier. The paper compares the performance of these different variants over multiple data sets and for the full range of misclassification costs and class frequencies. It concludes that although some of these variants are better than a single discriminative classifier, the right choice of training set distribution plus careful calibration are needed to make them competitive with multiple discriminative classifiers.
منابع مشابه
Discriminative vs. Generative Classifiers : An In-Depth Experimental Comparison using Cost Curves
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 compariso...
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 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 کاملDiscriminative Learning Using Boosted Generative Models
Discriminative learning, or learning for classification, is a common learning task that has been addressed in a variety of frameworks. One approach is to design a complex classifier, such as a support vector machine, that explicitly minimizes classification error. Alternatively, an ensemble of weak classifiers can be trained using boosting [4]. However, in some situations it may be desirable to...
متن کاملDiscriminative Learning Can Succeed Where Generative Learning Fails
Generative algorithms for learning classifiers use training data to separately estimate a probability model for each class. New items are classified by comparing their probabilities under these models. In contrast, discriminative learning algorithms try to find classifiers that perform well on all the training data. We show that there is a learning problem that can be solved by a discriminative...
متن کامل