80,088 research outputs found

    Low Size-Complexity Inductive Logic Programming: The East-West Challenge Considered as a Problem in Cost-Sensitive Classification

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    The Inductive Logic Programming community has considered proof-complexity and model-complexity, but, until recently, size-complexity has received little attention. Recently a challenge was issued "to the international computing community" to discover low size-complexity Prolog programs for classifying trains. The challenge was based on a problem first proposed by Ryszard Michalski, 20 years ago. We interpreted the challenge as a problem in cost-sensitive classification and we applied a recently developed cost-sensitive classifier to the competition. Our algorithm was relatively successful (we won a prize). This paper presents our algorithm and analyzes the results of the competition

    Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm

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    This paper introduces ICET, a new algorithm for cost-sensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors. ICET is compared here with three other algorithms for cost-sensitive classification - EG2, CS-ID3, and IDX - and also with C4.5, which classifies without regard to cost. The five algorithms are evaluated empirically on five real-world medical datasets. Three sets of experiments are performed. The first set examines the baseline performance of the five algorithms on the five datasets and establishes that ICET performs significantly better than its competitors. The second set tests the robustness of ICET under a variety of conditions and shows that ICET maintains its advantage. The third set looks at ICET's search in bias space and discovers a way to improve the search.Comment: See http://www.jair.org/ for any accompanying file

    Ensemble of Example-Dependent Cost-Sensitive Decision Trees

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    Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples and not only within classes. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. In previous works, some methods that take into account the financial costs into the training of different algorithms have been proposed, with the example-dependent cost-sensitive decision tree algorithm being the one that gives the highest savings. In this paper we propose a new framework of ensembles of example-dependent cost-sensitive decision-trees. The framework consists in creating different example-dependent cost-sensitive decision trees on random subsamples of the training set, and then combining them using three different combination approaches. Moreover, we propose two new cost-sensitive combination approaches; cost-sensitive weighted voting and cost-sensitive stacking, the latter being based on the cost-sensitive logistic regression method. Finally, using five different databases, from four real-world applications: credit card fraud detection, churn modeling, credit scoring and direct marketing, we evaluate the proposed method against state-of-the-art example-dependent cost-sensitive techniques, namely, cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision trees. The results show that the proposed algorithms have better results for all databases, in the sense of higher savings.Comment: 13 pages, 6 figures, Submitted for possible publicatio

    Population management of cone and seed insects in spruce seed orchards

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    Seed orchards have been established in order to produce high quality seeds for reforestation and forestation. However, seed production in spruce (Picea abies (L.) Karst.) seed orchards is severely hampered by cone- and seed-feeding insects. Therefore it is of great importance to find methods to reduce damages from insects. This thesis summarizes and discusses results presented in four papers concerning various methods and chemicals (insecticides and a pheromone) for damage reductions in spruce seed orchards. Area-wide application of the biological insecticide Turex 50 WP was shown to reduce damage by two of the four most serious pest species. Concerns were then raised that feeding by insects that are not affected by this insecticide may increase following its application, in response to the consequent increases in the availability of food and space, resulting in little no or difference in overall damage. A follow up study indicated that there would probably not be any problem with increased feeding by the larvae survived and that spraying of an insecticide not affecting all species would probably be cost effective. However, various species-related and abiotic factors (e.g. rain and temperature) affect the efficacy of insecticide treatments, both among and within years, and thus should be taken into account. A system that would be less sensitive to weather and also may affect all pest species and at the same time avoid affecting the surrounding environment is injectable systemic insecticides. In order to increase the cost efficiency a study was performed where insecticide was combined with the flower stimulating hormone gibberellin and successfully reduced damages and increased number of flowers. In order to know if and when an insecticide application should be carried out, pheromone for trapping insects is a useful tool. But in order to do so there must be a pheromone available. During the spring of 2009 a pheromone for C. strobilella was identified and synthesized. The study showed that the amount of pheromone released from the female was extremely low, 1 pg, so the male antenna is supersensitive in order to find females. This implies also that this species can be a good candidate for mating disruption
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