scholarly journals A Model Tree Generator (MTG) Framework for Simulating Hydrologic Systems: Application to Reservoir Routing

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2373
Author(s):  
Matin Rahnamay Naeini ◽  
Tiantian Yang ◽  
Ahmad Tavakoly ◽  
Bita Analui ◽  
Amir AghaKouchak ◽  
...  
Keyword(s):  
Decision Tree ◽  
Piecewise Linear ◽  
Continuous Variables ◽  
Hydrologic Systems ◽  
Model Tree ◽  
Search Mechanism ◽  
Splitting Criterion ◽  
Tree Algorithms ◽  
Tree Models ◽  
Computationally Intensive

Data-driven algorithms have been widely used as effective tools to mimic hydrologic systems. Unlike black-box models, decision tree algorithms offer transparent representations of systems and reveal useful information about the underlying process. A popular class of decision tree models is model tree (MT), which is designed for predicting continuous variables. Most MT algorithms employ an exhaustive search mechanism and a pre-defined splitting criterion to generate a piecewise linear model. However, this approach is computationally intensive, and the selection of the splitting criterion can significantly affect the performance of the generated model. These drawbacks can limit the application of MTs to large datasets. To overcome these shortcomings, a new flexible Model Tree Generator (MTG) framework is introduced here. MTG is equipped with several modules to provide a flexible, efficient, and effective tool for generating MTs. The application of the algorithm is demonstrated through simulation of controlled discharge from several reservoirs across the Contiguous United States (CONUS).

IMPROVING STABILITY OF DECISION TREES

2002 ◽  
Vol 16 (02) ◽  
pp. 145-159 ◽  
Author(s):  
MARK LAST ◽  
ODED MAIMON ◽  
EINAT MINKOV
Keyword(s):  
Decision Tree ◽  
Predictive Accuracy ◽  
Statistical Significance ◽  
Training Data ◽  
Tree Level ◽  
Information Theoretic ◽  
Tree Algorithms ◽  
Tree Models ◽  
The Stability ◽  
Information Theoretic Method

Decision-tree algorithms are known to be unstable: small variations in the training set can result in different trees and different predictions for the same validation examples. Both accuracy and stability can be improved by learning multiple models from bootstrap samples of training data, but the "meta-learner" approach makes the extracted knowledge hardly interpretable. In the following paper, we present the Info-Fuzzy Network (IFN), a novel information-theoretic method for building stable and comprehensible decision-tree models. The stability of the IFN algorithm is ensured by restricting the tree structure to using the same feature for all nodes of the same tree level and by the built-in statistical significance tests. The IFN method is shown empirically to produce more compact and stable models than the "meta-learner" techniques, while preserving a reasonable level of predictive accuracy.

A Practical Tutorial for Decision Tree Induction

2021 ◽  
Vol 54 (1) ◽  
pp. 1-38
Author(s):  
Víctor Adrián Sosa Hernández ◽  
Raúl Monroy ◽  
Miguel Angel Medina-Pérez ◽  
Octavio Loyola-González ◽  
Francisco Herrera
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Machine Learning Techniques ◽  
Evaluation Measures ◽  
Decision Tree Induction ◽  
Learning Techniques ◽  
Tree Models ◽  
Evaluation Measure ◽  
Main Components ◽  
Support Decision Making

Experts from different domains have resorted to machine learning techniques to produce explainable models that support decision-making. Among existing techniques, decision trees have been useful in many application domains for classification. Decision trees can make decisions in a language that is closer to that of the experts. Many researchers have attempted to create better decision tree models by improving the components of the induction algorithm. One of the main components that have been studied and improved is the evaluation measure for candidate splits. In this article, we introduce a tutorial that explains decision tree induction. Then, we present an experimental framework to assess the performance of 21 evaluation measures that produce different C4.5 variants considering 110 databases, two performance measures, and 10× 10-fold cross-validation. Furthermore, we compare and rank the evaluation measures by using a Bayesian statistical analysis. From our experimental results, we present the first two performance rankings in the literature of C4.5 variants. Moreover, we organize the evaluation measures into two groups according to their performance. Finally, we introduce meta-models that automatically determine the group of evaluation measures to produce a C4.5 variant for a new database and some further opportunities for decision tree models.

scholarly journals Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2849
Author(s):  
Sungbum Jun
Keyword(s):  
Decision Tree ◽  
Evolutionary Algorithm ◽  
Decision Trees ◽  
Manufacturing Systems ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Industrial Internet ◽  
Tree Models ◽  
Real World Datasets

Due to the recent advance in the industrial Internet of Things (IoT) in manufacturing, the vast amount of data from sensors has triggered the need for leveraging such big data for fault detection. In particular, interpretable machine learning techniques, such as tree-based algorithms, have drawn attention to the need to implement reliable manufacturing systems, and identify the root causes of faults. However, despite the high interpretability of decision trees, tree-based models make a trade-off between accuracy and interpretability. In order to improve the tree’s performance while maintaining its interpretability, an evolutionary algorithm for discretization of multiple attributes, called Decision tree Improved by Multiple sPLits with Evolutionary algorithm for Discretization (DIMPLED), is proposed. The experimental results with two real-world datasets from sensors showed that the decision tree improved by DIMPLED outperformed the performances of single-decision-tree models (C4.5 and CART) that are widely used in practice, and it proved competitive compared to the ensemble methods, which have multiple decision trees. Even though the ensemble methods could produce slightly better performances, the proposed DIMPLED has a more interpretable structure, while maintaining an appropriate performance level.

Novel Approach for Battery Type Determination: A Mere Electrical Alternative

2021 ◽  
Author(s):  
İsmail Can Dikmen ◽  
Teoman Karadağ
Keyword(s):  
Integrated Circuits ◽  
Decision Tree ◽  
Statistical Significance ◽  
High Capacity ◽  
Electrical Energy ◽  
Machine Learning Algorithms ◽  
Battery Management ◽  
Separation Function ◽  
Novel Approach ◽  
Tree Algorithms

Abstract Today, the storage of electrical energy is one of the most important technical challenges. The increasing number of high capacity, high-power applications, especially electric vehicles and grid energy storage, points to the fact that we will be faced with a large amount of batteries that will need to be recycled and separated in the near future. An alternative method to the currently used methods for separating these batteries according to their chemistry is discussed in this study. This method can be applied even on integrated circuits due to its ease of implementation and low operational cost. In this respect, it is also possible to use it in multi-chemistry battery management systems to detect the chemistry of the connected battery. For the implementation of the method, the batteries are connected to two different loads alternately. In this way, current and voltage values ​​are measured for two different loads without allowing the battery to relax. The obtained data is pre-processed with a separation function developed based on statistical significance. In machine learning algorithms, artificial neural network and decision tree algorithms are trained with processed data and used to determine battery chemistry with 100% accuracy. The efficiency and ease of implementation of the decision tree algorithm in such a categorization method are presented comparatively.

scholarly journals An Event Group Based Classification Framework for Multi-variate Sequential Data

2017 ◽  
Vol 21 ◽  
Author(s):  
Chao Sun ◽  
David Stirling
Keyword(s):  
Decision Tree ◽  
Classification Problem ◽  
Sequential Data ◽  
Feature Generation ◽  
Data Types ◽  
Nominal Data ◽  
Classification Framework ◽  
Improved Outcomes ◽  
Tree Algorithms ◽  
Industrial Problem

Decision tree algorithms were not traditionally considered for sequential data classification, mostly because feature generation needs to be integrated with the modelling procedure in order to avoid a localisation problem. This paper presents an Event Group Based Classification (EGBC) framework that utilises an X-of-N (XoN) decision tree algorithm to avoid the feature generation issue during the classification on sequential data. In this method, features are generated independently based on the characteristics of the sequential data. Subsequently an XoN decision tree is utilised to select and aggregate useful features from various temporal and other dimensions (as event groups) for optimised classification. This leads the EGBC framework to be adaptive to sequential data of differing dimensions, robust to missing data and accommodating to either numeric or nominal data types. The comparatively improved outcomes from applying this method are demonstrated on two distinct areas – a text based language identification task, as well as a honeybee dance behaviour classification problem. A further motivating industrial problem – hot metal temperature prediction, is further considered with the EGBC framework in order to address significant real-world demands.

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