2 edition of **Learning decision trees for loss minimization in multi-class problems** found in the catalog.

Learning decision trees for loss minimization in multi-class problems

Dragos D. Margineantu

- 197 Want to read
- 15 Currently reading

Published
**1999**
by Oregon State University, Dept. of Computer Science in [Corvallis, OR
.

Written in English

- Machine learning.,
- Decision trees.

Many machine learning applications require classifiers that minimize an asymmetric loss function rather than the raw misclassification rate. We study methods for modifying C4.5 to incorporate arbitrary loss matrices. One way to incorporate loss information into C4.5 is to manipulate the weights assigned to the examples from different classes. For 2-class problems, this works for any loss matrix, but for k > 2 classes, it is not sufficient. Nonetheless, we ask what is the set of class weights that best approximates an arbitrary k x k loss matrix, and we test and compare several methods: a wrapper method and some simple heuristics. The best method is a wrapper method that directly optimizes the loss using a holdout data set. We define complexity measure for loss matrices and show that this measure can predict when more efficient methods will suffice and when the wrapper method must be applied.

**Edition Notes**

Statement | Dragos D. Margineantu, Thomas G. Dietterich. |

Series | Technical report -- 99-30-03., Technical report (Oregon State University. Dept. of Computer Science) -- 99-30-03. |

Contributions | Dietterich, Thomas Glen., Oregon State University. Dept. of Computer Science. |

The Physical Object | |
---|---|

Pagination | 14 leaves : |

Number of Pages | 14 |

ID Numbers | |

Open Library | OL16125862M |

This book explains how Decision Trees work and how they can be combined into a Random Forest to reduce many of the common problems with decision trees, such as overfitting the training data. Several Dozen Visual Examples. Equations are great for really understanding every last detail of an algorithm. Solving Regression by Learning an Ensemble of Decision Rules. been developed to solve the optimal decision tree problem [9]. different loss functions and minimization techniques often.

Lecture 1 "Supervised Learning Setup" -Cornell CS Machine Learning for Decision Making SP17 Kilian Weinberger. The Learning Problem - Duration. G. Parmigiani, in International Encyclopedia of the Social & Behavioral Sciences, Binary Classification. Binary classification problems (Duda et al. ) consider assigning an individual to one of two categories, by measuring a series of example is medical diagnosis for a single medical condition (say disease vs. no disease) based on a battery of tests.

Decision Trees in Machine Learning Decision Tree models are created using 2 steps: Induction and Pruning. Induction is where we actually build the tree i.e set all of the hierarchical decision boundaries based on our data. Because of the nature of training decision trees . – Decision trees can express any function of the input attributes. – E.g., for Boolean functions, truth table row →path to leaf: T F A B F T B A B A xor B F F F F TT T F T TTF F FF T T T Continuous-input, continuous-output case: – Can approximate any function arbitrarily closely Trivially, there is a consistent decision tree for any File Size: KB.

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CiteSeerX — Learning Decision Trees for Loss Minimization In Multi-Class Problems. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Many machine learning applications require classifiers that minimize an asymmetric loss function rather than the raw misclassification rate.

Corpus ID: Learning decision trees for loss minimization in multi-class problems @inproceedings{MargineantuLearningDT, title={Learning decision trees for loss minimization in multi-class problems}, author={Dragos D. Margineantu and Thomas G. Dietterich}, year={} }. For 2-class problems, this works for any loss matrix, but for k > 2 classes, it is not sufficient.

Nonetheless, we ask what is the set of class weights that best approximates an arbitrary k x k loss matrix, and we test and compare several methods: a wrapper method and some simple by: Learning Decision Trees for Loss Minimization in Multi-Class Problems.

For 2-class problems, this works for any loss matrix, but for k. 2 classes, it is not sufficient. Nonetheless, we ask what is the set of class weights that best approximates an arbitrary k \Theta k loss matrix, and we test and compare several methods: a wrapper method.

Learning Decision T rees for Loss Minimization in Multi-Class Problems T ec hnical Rep ort Departmen t of Computer Science, Oregon State Univ ersit y. Dragos D. Marginean tu Departmen t of Computer Science, Oregon State Univ ersit y Corv allis, ORU.S.A. Thomas G.

Dietteric h Institut D'In v estigaci o en In tel lig encia. This idea is realized by means of a generalized risk minimization approach, using an extended loss function that compares precise predictions with set-valued observations.

As an illustration, we instantiate our meta learning technique for the problem of label ranking, in which the output space consists of all permutations of a fixed set of by: Building Decision Trees for the Multi-class Imbalance Problem The most popular of decisiontree learning algorithmis C [14].

Recently Hellinger distance decision trees (HDDTs) [4] havebeen proposedas LNAI - Building Decision Trees for the Multi-class Imbalance Problem. In this paper, we propose a powerful weak learner (Vector Decision Tree (VDT)) and a new Boosted Vector Decision Tree (BVDT) algorithm framework for the task of multi-class classification.

Unlike t Cited by: 1. CS Machine Learning CS Machine Learning Lecture 12 Milos Hauskrecht [email protected] Sennott Square Multi-class classification Decision trees CS Machine Learning Midterm exam Midterm Monday, March 2, • In-class (75 minutes) • closed book • material covered by Febru File Size: KB.

BVDT: A Boosted Vector Decision Tree Algorithm for Multi-class Classification Problems Article in International Journal of Pattern Recognition and Artificial Intelligence 31(5) October is an open and free Machine Learning course by the OpenDataScience community.

It is designed to perfectly balance theory and. Healthcare management: decision trees are used to make predictions in healthcare sectors like a decision tree model was used to predict the reason for developmentally delayed children.

Energy consumption: it can also be used to estimate the electricity used by an : Vineet Paulson. termed Alternating Decision Forests (ADFs), which formu-lates the training of Random Forests explicitly as a global loss minimization problem.

During training, the losses are minimized via keeping an adaptive weight distribution over the training samples, similar to Boosting methods. In order to keep the method as ﬂexible and general as. In short, yes, you can use decision trees for this problem. However there are many other ways to predict the result of multiclass problems.

If you want to use decision trees one way of doing it could be to assign a unique integer to each of your classes. Learning a decision tree from data is a difﬁcult optimization problem. The most widespreadalgorithminpractice,datingtothes,is basedonagreedygrowth of the tree structure by recursively splitting nodes, and possibly pruning back the ﬁnal tree.

The parameters (decision function) of an internal node are approxi. Decision Trees in Machine Learning - Decision tree method is a commonly used data mining method for establishing classification systems based on several covari Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising.

CS Machine Learning Decision trees • Decision tree model: – Split the space recursivel y according to inputs in x – Classify at the bottom of the tree x3 0 x t f x1 0 0 x2 ttff Example: Binary classification Binary attributes 0 10 x1, x2, x3 {0,1} classify x2 0 CS Machine Learning Decision trees • Decision tree model:File Size: KB.

Decision trees, one of the simplest and yet most useful Machine Learning structures. Decision trees, as the name implies, are trees of decisions. An updated version of this article can be found here.

Decision tree learning example • Induced tree (from examples) • Cannot make it more complex than what the data supports. Decision Tree Learning Algorithm The Trained Tree Classification Methods 1. Information Gain • ID3 • C • C 5 • J 48 2. Gini Index • SPRINT • SLIQ are better for different loss matrices.

1 Pruning Decision Trees Decision trees are a widely used symbolic modeling technique for classification tasks in machine learning. The most common approach to constructing decision tree classifiers is to grow a full tree and prune it back. Pruning is desirable be-Cited by:.

DOI: /S(00) Corpus ID: Multiple Criteria for Evaluating Machine Learning Algorithms for Land Cover Classification from Satellite Data @inproceedings{DeFriesMultipleCF, title={Multiple Criteria for Evaluating Machine Learning Algorithms for Land Cover Classification from Satellite Data}, author={Ruth S.

DeFries and Jonathan .2 Learning Decision Trees A decision tree is a binary tree in which the internal nodes are labeled with variables and the leafs are labeled with either −1 or +1.

And the left and right edges corresponding to any internal node is labeled −1 and +1 respectively. We can think of the decision tree as deﬁning a .Decision tree learning: | | | |Machine learning| and| |data mining| | World Heritage Encyclopedia, the aggregation of the largest online encyclopedias available.