Results 11 to 20 of about 883,011 (303)
, 1990 This paper describes experiments, on two domains, to investigate the effect of averaging over predictions of multiple decision trees, instead of using a single tree. Other authors have pointed out theoretical and commonsense reasons for preferring the multiple tree approach.
Chris Carter, Suk Wah Kwok
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Bivariate decision trees [PDF]
, 1997 Decision tree methods constitute an important and much used technique for classification problems. When such trees are used in a Datamining and Knowledge Discovery context, ease of interpretation of the resulting trees is an important requirement to be met.
Rob Potharst +2 more
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Decision Rules Derived from Optimal Decision Trees with Hypotheses
Entropy, 2021 Conventional decision trees use queries each of which is based on one attribute. In this study, we also examine decision trees that handle additional queries based on hypotheses.
Mohammad Azad +4 more
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Predicting Credit Scores with Boosted Decision Trees
Forecasting, 2022 Credit scoring models help lenders decide whether to grant or reject credit to applicants. This paper proposes a credit scoring model based on boosted decision trees, a powerful learning technique that aggregates several decision trees to form a ...
João A. Bastos
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Data‐driven performance metrics for neural network learning
International Journal of Adaptive Control and Signal Processing, EarlyView., 2023 Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state ...
Angelo Alessandri +2 more
wiley +1 more source
Evolutionary Learning of Interpretable Decision Trees
IEEE Access, 2023 In the last decade, reinforcement learning (RL) has been used to solve several tasks with human-level performance. However, there is a growing demand for interpretable RL, i.e., there is the need to understand how a RL agent works and the rationale of ...
Leonardo L. Custode, Giovanni Iacca
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Tree in Tree: from Decision Trees to Decision Graphs
, 2021 Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process. This paper introduces Tree in Tree decision graph (TnT), a framework that extends the conventional decision tree to a more generic and powerful directed acyclic graph.
Zhu, Bingzhao, Shoaran, Mahsa
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FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees [PDF]
Judgment and Decision Making, 2017 Fast-and-frugal trees (FFTs) are simple algorithms that facilitate efficient and accurate decisions based on limited information. But despite their successful use in many applied domains, there is no widely available toolbox that allows anyone to easily ...
Nathaniel D. Phillips +3 more
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A Bi-criteria optimization model for adjusting the decision tree parameters
Kuwait Journal of Science, 2022 Decision trees play a very important role in knowledge representation because of its simplicity and self-explanatory nature. We study the optimization of the parameters of the decision trees to find a shorter as well as more accurate decision tree ...
Mohammad Azad, Mikhail Moshkov
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IEEE Transactions on Knowledge and Data Engineering, 2017 Uncovering causal relationships in data is a major objective of data analytics. Causal relationships are normally discovered with designed experiments, e.g. randomised controlled trials, which, however are expensive or infeasible to be conducted in many cases.
Jiuyong Li +4 more
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