20,419 research outputs found
Inducing safer oblique trees without costs
Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly higher than in the other class. Several authors have tackled this problem by developing cost-sensitive decision tree learning algorithms or have suggested ways of changing the
distribution of training examples to bias the decision tree learning process so as to take account of costs. A prerequisite for applying such algorithms is the availability of costs of misclassification.
Although this may be possible for some applications, obtaining reasonable estimates of costs of misclassification is not easy in the area of safety.
This paper presents a new algorithm for applications where the cost of misclassifications cannot be quantified, although the cost of misclassification in one class is known to be significantly higher than in another class. The algorithm utilizes linear discriminant analysis to identify oblique relationships between continuous attributes and then carries out an appropriate modification to ensure that the resulting tree errs on the side of safety. The algorithm is evaluated with respect to one of the best known cost-sensitive algorithms (ICET), a well-known oblique decision tree algorithm (OC1) and an algorithm that utilizes robust linear programming
CSNL: A cost-sensitive non-linear decision tree algorithm
This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision nodes and utilizes discriminant analysis to construct nonlinear decision trees that take account of costs of misclassification.
The performance of the algorithm is evaluated by applying it to seventeen datasets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the datasets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using nonlinear decision nodes.
The performance of the algorithm is evaluated by applying it to seventeen data sets and the results are
compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date.
The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the data sets and is considerably faster.
The use of bagging with CSNL further enhances its performance showing the significant benefits of using non-linear decision nodes
A survey of cost-sensitive decision tree induction algorithms
The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field
Cost-Sensitive Decision Tree Pruning: Use of the ROC Curve
This paper discusses a revised form of decision tree pruning that is sensitive to the relative costs of the misclassification of examples. A brief overview of existing decision tree pruning methods is given together with the rationale behind these techniques. Then, the two types of misclassifications, false negatives and false positives, are defined and related to three concepts from statistical pattern recognition: the receiver operating characteristic (ROC) curve; statistical hypothesis testing; and the Neyman-Pearson method. Details of the implementation of two cost-sensitive pruning algorithms, based on the well known Pessimistic and Minimum Error pruning techniques, are discussed. Results are then presented for both these techniques on two machine learning datasets and related to ROC curves and the Neyman-Pearson method. Thus we show that decision trees can be made to conform to specified operating criteria given in terms of the probabilities of false negatives and false positives. As a result of this analysis, it is noted that, on the data sets chosen, unequal misclassification costs actually increased the overall accuracy of the classification scheme. It is concluded that the application of the ROC curve, from statistical pattern recognition to machine learning, and to decision tree pruning in particular, can provide increased flexibility and accurac
Integrating Learning from Examples into the Search for Diagnostic Policies
This paper studies the problem of learning diagnostic policies from training
examples. A diagnostic policy is a complete description of the decision-making
actions of a diagnostician (i.e., tests followed by a diagnostic decision) for
all possible combinations of test results. An optimal diagnostic policy is one
that minimizes the expected total cost, which is the sum of measurement costs
and misdiagnosis costs. In most diagnostic settings, there is a tradeoff
between these two kinds of costs. This paper formalizes diagnostic decision
making as a Markov Decision Process (MDP). The paper introduces a new family of
systematic search algorithms based on the AO* algorithm to solve this MDP. To
make AO* efficient, the paper describes an admissible heuristic that enables
AO* to prune large parts of the search space. The paper also introduces several
greedy algorithms including some improvements over previously-published
methods. The paper then addresses the question of learning diagnostic policies
from examples. When the probabilities of diseases and test results are computed
from training data, there is a great danger of overfitting. To reduce
overfitting, regularizers are integrated into the search algorithms. Finally,
the paper compares the proposed methods on five benchmark diagnostic data sets.
The studies show that in most cases the systematic search methods produce
better diagnostic policies than the greedy methods. In addition, the studies
show that for training sets of realistic size, the systematic search algorithms
are practical on todays desktop computers
Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction
For large, real-world inductive learning problems, the number of training
examples often must be limited due to the costs associated with procuring,
preparing, and storing the training examples and/or the computational costs
associated with learning from them. In such circumstances, one question of
practical importance is: if only n training examples can be selected, in what
proportion should the classes be represented? In this article we help to answer
this question by analyzing, for a fixed training-set size, the relationship
between the class distribution of the training data and the performance of
classification trees induced from these data. We study twenty-six data sets
and, for each, determine the best class distribution for learning. The
naturally occurring class distribution is shown to generally perform well when
classifier performance is evaluated using undifferentiated error rate (0/1
loss). However, when the area under the ROC curve is used to evaluate
classifier performance, a balanced distribution is shown to perform well. Since
neither of these choices for class distribution always generates the
best-performing classifier, we introduce a budget-sensitive progressive
sampling algorithm for selecting training examples based on the class
associated with each example. An empirical analysis of this algorithm shows
that the class distribution of the resulting training set yields classifiers
with good (nearly-optimal) classification performance
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