Boosting Cost-Sensitive Trees
نویسندگان
چکیده
This paper explores two techniques for boosting cost-sensitive trees. The two techniques diier in whether the misclassiication cost information is utilized during training. We demonstrate that each of these techniques is good at diierent aspects of cost-sensitive classiications. We also show that both techniques provide a means to overcome the weaknesses of their base cost-sensitive tree induction algorithm.
منابع مشابه
Boosting Trees for Cost-Sensitive Classi cations
This paper explores two boosting techniques for cost-sensitive tree classi cations in the situation where misclassi cation costs change very often. Ideally, one would like to have only one induction, and use the induced model for di erent misclassi cation costs. Thus, it demands robustness of the induced model against cost changes. Combining multiple trees gives robust predictions against this ...
متن کاملBoosting Trees for Cost-Sensitive Classifications
This paper explores two boosting techniques for cost-sensitive tree classiications in the situation where misclassiication costs change very often. Ideally, one would like to have only one induction, and use the induced model for diierent misclassiication costs. Thus, it demands robustness of the induced model against cost changes. Combining multiple trees gives robust predictions against this ...
متن کاملAccelerated Gradient Boosting
Gradient tree boosting is a prediction algorithm that sequentially produces a model in the form of linear combinations of decision trees, by solving an infinite-dimensional optimization problem. We combine gradient boosting and Nesterov’s accelerated descent to design a new algorithm, which we call AGB (for Accelerated Gradient Boosting). Substantial numerical evidence is provided on both synth...
متن کاملOutlier Detection by Boosting Regression Trees
A procedure for detecting outliers in regression problems is proposed. It is based on information provided by boosting regression trees. The key idea is to select the most frequently resampled observation along the boosting iterations and reiterate after removing it. The selection criterion is based on Tchebychev’s inequality applied to the maximum over the boosting iterations of ...
متن کاملCost-sensitive Boosting with p-norm Loss Functions and its Applications
In practical applications of classification, there are often varying costs associated with different types of misclassification (e.g. fraud detection, anomaly detection and medical diagnosis), motivating the need for the so-called ”cost-sensitive” classification. In this paper, we introduce a family of novel boosting methods for cost-sensitive classification by applying the theory of gradient b...
متن کامل