4,171 research outputs found
Boosting the accuracy of hedonic pricing models
Hedonic pricing models attempt to model a relationship between object attributes andthe object's price. Traditional hedonic pricing models are often parametric models that sufferfrom misspecification. In this paper we create these models by means of boosted CARTmodels. The method is explained in detail and applied to various datasets. Empirically,we find substantial reduction of errors on out-of-sample data for two out of three datasetscompared with a stepwise linear regression model. We interpret the boosted models by partialdependence plots and relative importance plots. This reveals some interesting nonlinearitiesand differences in attribute importance across the model types.pricing;marketing;data mining;conjoint analysis;ensemble learning;gradient boosting;hedonic pricing
TreeGrad: Transferring Tree Ensembles to Neural Networks
Gradient Boosting Decision Tree (GBDT) are popular machine learning
algorithms with implementations such as LightGBM and in popular machine
learning toolkits like Scikit-Learn. Many implementations can only produce
trees in an offline manner and in a greedy manner. We explore ways to convert
existing GBDT implementations to known neural network architectures with
minimal performance loss in order to allow decision splits to be updated in an
online manner and provide extensions to allow splits points to be altered as a
neural architecture search problem. We provide learning bounds for our neural
network.Comment: Technical Report on Implementation of Deep Neural Decision Forests
Algorithm. To accompany implementation here:
https://github.com/chappers/TreeGrad. Update: Please cite as: Siu, C. (2019).
"Transferring Tree Ensembles to Neural Networks". International Conference on
Neural Information Processing. Springer, 2019. arXiv admin note: text overlap
with arXiv:1909.1179
Vote-boosting ensembles
Vote-boosting is a sequential ensemble learning method in which the
individual classifiers are built on different weighted versions of the training
data. To build a new classifier, the weight of each training instance is
determined in terms of the degree of disagreement among the current ensemble
predictions for that instance. For low class-label noise levels, especially
when simple base learners are used, emphasis should be made on instances for
which the disagreement rate is high. When more flexible classifiers are used
and as the noise level increases, the emphasis on these uncertain instances
should be reduced. In fact, at sufficiently high levels of class-label noise,
the focus should be on instances on which the ensemble classifiers agree. The
optimal type of emphasis can be automatically determined using
cross-validation. An extensive empirical analysis using the beta distribution
as emphasis function illustrates that vote-boosting is an effective method to
generate ensembles that are both accurate and robust
Totally Corrective Multiclass Boosting with Binary Weak Learners
In this work, we propose a new optimization framework for multiclass boosting
learning. In the literature, AdaBoost.MO and AdaBoost.ECC are the two
successful multiclass boosting algorithms, which can use binary weak learners.
We explicitly derive these two algorithms' Lagrange dual problems based on
their regularized loss functions. We show that the Lagrange dual formulations
enable us to design totally-corrective multiclass algorithms by using the
primal-dual optimization technique. Experiments on benchmark data sets suggest
that our multiclass boosting can achieve a comparable generalization capability
with state-of-the-art, but the convergence speed is much faster than stage-wise
gradient descent boosting. In other words, the new totally corrective
algorithms can maximize the margin more aggressively.Comment: 11 page
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