Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
22,304
result(s) for
"decision-trees learning"
Sort by:
Semi-supervised self-training for decision tree classifiers
We consider semi-supervised learning, learning task from both labeled and unlabeled instances and in particular, self-training with decision tree learners as base learners. We show that standard decision tree learning as the base learner cannot be effective in a self-training algorithm to semi-supervised learning. The main reason is that the basic decision tree learner does not produce reliable probability estimation to its predictions. Therefore, it cannot be a proper selection criterion in self-training. We consider the effect of several modifications to the basic decision tree learner that produce better probability estimation than using the distributions at the leaves of the tree. We show that these modifications do not produce better performance when used on the labeled data only, but they do benefit more from the unlabeled data in self-training. The modifications that we consider are Naive Bayes Tree, a combination of No-pruning and Laplace correction, grafting, and using a distance-based measure. We then extend this improvement to algorithms for ensembles of decision trees and we show that the ensemble learner gives an extra improvement over the adapted decision tree learners.
Journal Article
Application of machine learning methods in forest ecology: recent progress and future challenges
The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author.
Journal Article
Online monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniques
With modern data collection system and computers used for on-line process monitoring and fault identification in manufacturing processes, it is common to monitor more than one correlated process variables simultaneously. The main problems in most multivariate control charts (e.g.,
T
2
charts, MCUSUM charts, MEWMA charts) are that they cannot give direct information on which variable or subset of variables caused the out-of-control signals. A Decision Tree (DT) learning based model for bivariate process mean shift monitoring and fault identification is proposed in this paper under the assumption of constant variance-covariance matrix. Two DT classifiers based on the C5.0 algorithm are built, one for process monitoring and the other for fault identification. Simulation results show that the proposed model can not only detect the mean shifts but also give information on the variable or subset of variables that cause the out-of-control signals and its/their deviate directions. Finally a bivariate process example is presented and compared with the results of an existing model.
Journal Article
Logic-optimization behavior tree algorithm for enhanced autonomous underwater vehicle cooperative decision-making
The effective generation of decision control rules for intelligent decision-making and behavior planning in Autonomous Underwater Vehicles (AUVs) is crucial. However, traditional decision control rules often rely on manual design, leading to complications and delays. To address these challenges, this chapter proposes a Logic-Optimization Decision-Making Behavior Tree (Logic-DBT) algorithm specifically for multi-AUV. The algorithm utilizes AUV simulations to generate behavior planning data, incorporating a decision tree learning mechanism. Continuous attributes are discretized to facilitate the design of a decision tree for cooperative behavior planning among AUVs. A ternary logic structure is established, with control elements at its core, integrating state judgment and action elements. This structure is then used to create a Decision-Making Behavior Tree (DBT) based on Decision Trees (DTs) by combining temporal logic and constraint modeling. The decision behavior tree’s structure is simplified through a logical operation optimization method, ensuring operational simplicity, timeliness, and reachability. Simulation experiments on automatic behavior control for multi-AUV cooperative tasks demonstrate that the Logic-DBT algorithm provides significant advantages in terms of rapid behavior control response, precise timing logic, and safe, effective operations.
Journal Article
Optimal constraint-based decision tree induction from itemset lattices
In this article we show that there is a strong connection between decision tree learning and local pattern mining. This connection allows us to solve the computationally hard problem of finding
optimal
decision trees in a wide range of applications by post-processing a set of patterns: we use local patterns to construct a global model. We exploit the connection between constraints in pattern mining and constraints in decision tree induction to develop a framework for categorizing decision tree mining constraints. This framework allows us to determine which model constraints can be pushed deeply into the pattern mining process, and allows us to improve the state-of-the-art of optimal decision tree induction.
Journal Article
Sovereign Debt and Currency Crises Prediction Models Using Machine Learning Techniques
Sovereign debt and currencies play an increasingly influential role in the development of any country, given the need to obtain financing and establish international relations. A recurring theme in the literature on financial crises has been the prediction of sovereign debt and currency crises due to their extreme importance in international economic activity. Nevertheless, the limitations of the existing models are related to accuracy and the literature calls for more investigation on the subject and lacks geographic diversity in the samples used. This article presents new models for the prediction of sovereign debt and currency crises, using various computational techniques, which increase their precision. Also, these models present experiences with a wide global sample of the main geographical world zones, such as Africa and the Middle East, Latin America, Asia, Europe, and globally. Our models demonstrate the superiority of computational techniques concerning statistics in terms of the level of precision, which are the best methods for the sovereign debt crisis: fuzzy decision trees, AdaBoost, extreme gradient boosting, and deep learning neural decision trees, and for forecasting the currency crisis: deep learning neural decision trees, extreme gradient boosting, random forests, and deep belief network. Our research has a large and potentially significant impact on the macroeconomic policy adequacy of the countries against the risks arising from financial crises and provides instruments that make it possible to improve the balance in the finance of the countries.
Journal Article
Analyzing genetic diseases using multimedia processing techniques associative decision tree-based learning and Hopfield dynamic neural networks from medical images
Genetic diseases are the most common next-generation diseases because of the improper mutation of the genes and DNA. These genetic diseases are failed to predict with an accurate manner in the beginning stage by using the particular genes and related information. So, the genetic diseases are identified in the medical systems by utilizing the hybridization of multimedia techniques such as big data and related soft computing techniques.Initially, the genetic disease-related medical images are collected from healthcare sectors, and from the genetic image, various genetic data are collected from the large amount of datasets in which the major challenge is too high dimensionality that increases the complexity of the genetic disease prediction system. So, in this paper the complexity of the system is reduced by using the associative decision tree-based learning and Hopfield dynamic neural networks (HDNN). After collecting the data from the various resources, the immune clonal selection algorithm approach is used to remove inconsistent data and minimize the dimensionality of data. The selected features are trained by the proposed associative decision tree approach which helps to compare with the testing features using the HDNN that successfully recognize the genetic disease-based features effectively. The excellence of the system is measured with the aid of the experimental outcomes that are corresponding to the forecasting methods such as greedy algorithm, rough set method and artificial bee colony, and the comparison is made with the avail of the accuracy, sensitivity and specificity.
Journal Article
Learning rules of a card game from video
This paper presents a framework for automatically learning rules of a simple game of cards using data from a vision system observing the game being played. Incremental learning of object and protocol models from video, for use by an artificial cognitive agent, is presented. iLearn--a novel algorithm for inducing univariate decision trees for symbolic datasets is introduced. iLearn builds the decision tree in an incremental way allowing automatic learning of rules of the game.[PUBLICATION ABSTRACT]
Journal Article
Robot Navigation by Waypoints
In this paper we propose a novel waypoint-based robot navigation method that combines reactive and deliberative actions. The approach uses reactive exploration to generate waypoints that can then be used by a deliberative system to plan future movements through the same environment. The waypoints are used largely to provide the interface between reactive and deliberative navigation and a range of methods could be used for either type of navigation. In the current work, an incremental decision tree method is used to navigate the robot reactively from the specified initial position to its destination avoiding obstacles in its path and a genetic algorithm method is used to perform the deliberative navigation. The new method is shown to have a number of practical advantages. Firstly, in contrast with many deliberative approaches, complete knowledge of the environment is not required, nor is it necessary to make assumptions regarding the geometry of obstacles. Secondly, the presence of a reactive navigator means it is always possible to continue directed movements in unknown or changing environments or when time constraints become particularly demanding. Thirdly, the use of waypoints allows escape from certain obstacle configurations that would normally trap robots navigated under the control of purely reactive methods. In addition, the results presented in this paper from a number of realistic simulated environments show that the adoption of waypoints significantly reduces the time to calculate a deliberative path.
Journal Article