AdaTree: Boosting a Weak Classifier into a Decision Tree

Grossmann, Etienne

doi:10.1109/cvpr.2004.296

Cited by 27 publications

(20 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RandomForest [13] is another popular ensemble approach; it applies the bagging technique to a subset of attributes as well as training samples that are randomly selected, to generate new trees via iterations and to vote in the best performing tree amongst the peers in "the forest". The AdaTree method [14], the Probabilistic Boosting-Tree method [15], and a combined Bayesian model approach [16] are some new additions to the ensemble trend development.…”

Section: Related Workmentioning

confidence: 99%

Identify Error-Sensitive Patterns by Decision Tree

2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. When errors are inevitable during data classification, finding a particular part of the classification model which may be more susceptible to error than others, when compared to finding an Achilles' heel of the model in a casual way, may help uncover specific error-sensitive value patterns and lead to additional error reduction measures. As an initial phase of the investigation, this study narrows the scope of problem by focusing on decision trees as a pilot model, develops a simple and effective tagging method to digitize individual nodes of a binary decision tree for node-level analysis, to link and track classification statistics for each node in a transparent way, to facilitate the identification and examination of the potentially "weakest" nodes and error-sensitive value patterns in decision trees, to assist cause analysis and enhancement development.This digitization method is not an attempt to re-develop or transform the existing decision tree model, but rather, a pragmatic node ID formulation that crafts numeric values to reflect the tree structure and decision making paths, to expand post-classification analysis to detailed nodelevel. Initial experiments have shown successful results in locating potentially high-risk attribute and value patterns; this is an encouraging sign to believe this study worth further exploration.

show abstract

Section: Related Workmentioning

confidence: 99%

Identify Error-Sensitive Patterns by Decision Tree

2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…In fact, starting with several thousands of images, we can obtain sub-clusters of size less than 100 by expanding the tree to a depth no greater than 3 or 4, which can be done in a few minutes. 5 …”

Section: Computational Complexity Of Learningmentioning

confidence: 99%

Learning a Dynamic Classification Method to Detect Faces and Identify Facial Expression

Isukapalli¹,

Elgammal

Greiner³

2005

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract.While there has been a great deal of research in face detection and recognition, there has been very limited work on identifying the expression on a face. Many current face detection projects use a [Viola/Jones] style "cascade" of Adaboost-based classifiers to interpret (sub)images -e.g., to identify which regions contain faces. We extend this method by learning a decision tree of such classifiers (DTC): While standard cascade classification methods will apply the same sequence of classifiers to each image, our DTC is able to select the most effective classifier at every stage, based on the outcomes of the classifiers already applied. We use DTC not only to detect faces in a test image, but to identify the expression on each face.

show abstract

“…Most of the works try to improve AdaBoost [10][11][12][13][14][15]. For instance, researchers implement a decision tree as a weak learner and use this tree to construct a strong classifier.…”

Section: Related Workmentioning

confidence: 99%

Thresholding a Random Forest Classifier

Baumann

Ehlers

et al. 2014

Advances in Visual Computing

View full text Add to dashboard Cite

Abstract. The original Random Forest derives the final result with respect to the number of leaf nodes voted for the corresponding class. Each leaf node is treated equally and the class with the most number of votes wins. Certain leaf nodes in the topology have better classification accuracies and others often lead to a wrong decision. Also the performance of the forest for different classes differs due to uneven class proportions. In this work, a novel voting mechanism is introduced: each leaf node has an individual weight. The final decision is not determined by majority voting but rather by a linear combination of individual weights leading to a better and more robust decision. This method is inspired by the construction of a strong classifier using a linear combination of small rules of thumb (AdaBoost). Small fluctuations which are caused by the use of binary decision trees are better balanced. Experimental results on several datasets for object recognition and action recognition demonstrate that our method successfully improves the classification accuracy of the original Random Forest algorithm.

show abstract

AdaTree: Boosting a Weak Classifier into a Decision Tree

Cited by 27 publications

References 0 publications

Identify Error-Sensitive Patterns by Decision Tree

Identify Error-Sensitive Patterns by Decision Tree

Learning a Dynamic Classification Method to Detect Faces and Identify Facial Expression

Thresholding a Random Forest Classifier

Contact Info

Product

Resources

About