Construction of Near-Optimal Axis-Parallel Decision Trees Using a Differential-Evolution-Based Approach

Rivera-López, Rafael; Canul-Reich, Juana

doi:10.1109/access.2017.2788700

Cited by 40 publications

(24 citation statements)

References 64 publications

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“…The differential evolution algorithm is a powerful and effective evolutionary algorithm firstly proposed by Storn and Price [32]. It is an algorithm that can be implemented easily with very few parameters [33]. It has been proven to be superior to the algorithms such as the genetic algorithm, evolution strategy, adaptive simulated annealing [32], and particle swarm optimization [34], [35].…”

Section: B Differential Evolution Algorithmmentioning

confidence: 99%

A Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing Metal Mines Production Process

Xun-hong

Liu

et al. 2020

IEEE Access

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Production process optimization is an indispensable step in industrial production. The optimization of the metal mines production process (MMPP) can increase production efficiency and thus promote the utilization rate of the metal mineral resources in the frame work of sustainable development. This study establishes a multi-objective optimization model for optimizing the MMPP by maximizing economic and resource benefits. To get better non-dominated Pareto optimal solutions, an improved non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. The symmetric Latin hypercube design is adopted to generate the initial population with high diversity. The mutation and crossover of the differential evolution algorithms are introduced into the NSGA-II to replace the genetic algorithm for improving convergence. The control parameters of the mutation scale factor and crossover rate of the differential evolution algorithm are adaptively adjusted to improve the diversity of candidate solutions. To verify the performance of the improved NSGA-II, four test functions from the ZDT series functions are chosen for experimentation. The experimental results indicate that the improved NSGA-II outperforms the comparative algorithms in diversity and convergence. Moreover, the application of the proposed method to the Yinshan copper mines shows that the improved NSGA-II is effective in optimizing the MMPP and a reliable method in promoting utilization rate of metal mineral resources in the framework of sustainable development. INDEX TERMS Metal mines production process, multi-objective optimization, symmetric Latin hypercube design, differential evolution, parameter adaptation, improved NSGA-II.

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Section: B Differential Evolution Algorithmmentioning

confidence: 99%

A Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing Metal Mines Production Process

Xun-hong

Liu

et al. 2020

IEEE Access

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“…To cluster categorical data, we use formula (13) to replace formula (7) in step 4 and step 7 of Algorithm 1 and formula (15) to formula (10) in step 6.…”

Section: Categorical Featurementioning

confidence: 99%

“…Despite practical success, the optimal construction of decision trees has been theoretically proven to be NPcomplete [11]. In order to avoid the local optimal solution, some researchers adopted evolutionary algorithms to build decision trees [12][13][14]. However, due to the time complexity, the most popular algorithms, such as ID3 [15], C4.5 [16], and CART [17], and their various modifications [18] are greedy by nature and construct the decision tree in a top-down, recursive manner.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multiway Splits Decision Tree for Multiple Types of Data

Liu

Wen

Sun³

et al. 2020

Mathematical Problems in Engineering

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Classical decision trees such as C4.5 and CART partition the feature space using axis-parallel splits. Oblique decision trees use the oblique splits based on linear combinations of features to potentially simplify the boundary structure. Although oblique decision trees have higher generalization accuracy, most oblique split methods are not directly conducive to the categorical data and are computationally expensive. In this paper, we propose a multiway splits decision tree (MSDT) algorithm, which adopts feature weighting and clustering. This method can combine multiple numerical features, multiple categorical features, or multiple mixed features. Experimental results show that MSDT has excellent performance for multiple types of data.

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“…The starting node is called a root node, the internal nodes are the set of nodes Child1 to Child3, bottom nodes are class labels or leaf nodes. [24] In the DT model, to construct a reasonably good tree and to define attributes for each root note, Grini impurity (cost function) is given as follows: [24].…”

Section: B: Decision Treesmentioning

confidence: 99%

Self-Identification Respiratory Disorder Based on Continuous Wave Radar Sensor System

et al. 2019

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Contactless vital signs detection, based on the Doppler radar sensor system, has opened a great opportunity in biomedical applications. The radar sensor system can be used to provide the respiratory information of people without disturbing their comfort. This sensor system promises high accuracy in measuring breathing disorders as it escapes the touching sensors which might cause discomfort to the user and negatively affect their sleeping habits. Moreover, this sensor system does not require any special environment or depend on temperature and light conditions. In this paper, we propose a model to the end users; this model is to be built based on neural networks. Our proposed system can diagnose whether a person has a low, normal, or high breathing rate. This model can also be extended to more specific categories to help doctors to determine breathing disorders in patients. In this paper, a continuous wave radar sensor system, based on a vector network analyzer (VNA), is used to measure the breathing rate remotely. The measured signal from this radar sensor system is then processed for further purposes. Different extracted feature methods are implemented to obtain the breathing rate from the non-contact radar sensor system. A model based on the machine learning technique is investigated to classify the breathing disorder. A total of 31 people who were asked to perform low/normal/high breathing were measured by the CW radar sensor. The measured data were also used to build a machine learning based model. The breathing rate measured by the CW radar sensor system is compared with the reference measurement by the five-point touching Shimmer sensor. The results of the breathing rate are compatible. Two main time-frequency (TF) extraction feature methods, short-time Fourier transform (STFT) and continuous wavelet transform (CWT), were implemented in the proposed system. Under these extraction techniques, some classification approaches were employed and have shown high accuracy in categorizing the respiratory types. The research shows the possibility of building an artificial intelligence (AI) module for a non-contact radar sensor system to inform the end user of their breathing situation. This research enables a smarter and more friendly remote-detecting vital signs sensor system.

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Construction of Near-Optimal Axis-Parallel Decision Trees Using a Differential-Evolution-Based Approach

Cited by 40 publications

References 64 publications

A Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing Metal Mines Production Process

A Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing Metal Mines Production Process

A Novel Multiway Splits Decision Tree for Multiple Types of Data

Self-Identification Respiratory Disorder Based on Continuous Wave Radar Sensor System

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