scholarly journals Random Fuzzy Granular Decision Tree

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Wei Li ◽  
Xiaoyu Ma ◽  
Yumin Chen ◽  
Bin Dai ◽  
Runjing Chen ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Decision Tree ◽  
High Efficiency ◽  
Information Gain ◽  
Optimal Solution ◽  
Numerical Data ◽  
Classification Problem ◽  
Classification Problems ◽  
Local Optimal Solution ◽  
Information Gain Ratio

In this study, the classification problem is solved from the view of granular computing. That is, the classification problem is equivalently transformed into the fuzzy granular space to solve. Most classification algorithms are only adopted to handle numerical data; random fuzzy granular decision tree (RFGDT) can handle not only numerical data but also nonnumerical data like information granules. Measures can be taken in four ways as follows. First, an adaptive global random clustering (AGRC) algorithm is proposed, which can adaptively find the optimal cluster centers and maximize the ratio of interclass standard deviation to intraclass standard deviation, and avoid falling into local optimal solution; second, on the basis of AGRC, a parallel model is designed for fuzzy granulation of data to construct granular space, which can greatly enhance the efficiency compared with serial granulation of data; third, in the fuzzy granular space, we design RFGDT to classify the fuzzy granules, which can select important features as tree nodes based on information gain ratio and avoid the problem of overfitting based on the pruning algorithm proposed. Finally, we employ the dataset from UC Irvine Machine Learning Repository for verification. Theory and experimental results prove that RFGDT has high efficiency and accuracy and is robust in solving classification problems.

Application Research on Traffic Modal Choice Based on Decision Tree Algorithm

2011 ◽  
Vol 97-98 ◽  
pp. 843-848
Author(s):  
Zheng Hong Peng ◽  
Xin Luan
Keyword(s):  
Decision Tree ◽  
Information Gain ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Tree Model ◽  
Modal Choice ◽  
Sample Data ◽  
Urbanization In China ◽  
Decision Tree Method ◽  
Information Gain Ratio

With the rapid development of urbanization in china, the contradiction between transport, environment and population growth is becoming more and more pronounced, which offers higher demands for transport planning. This article mainly describes the application of decision tree learning algorithm in traffic modal choice. First preprocess the sample data, then calculate and analyze the information gain ratio, and finally we will build a decision tree model. The results show that the rules obtained by decision tree method have some practical value in the analysis of traffic modal choice.

An Improved SPRINT Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1685-1690 ◽  
Author(s):  
Zhi Kang Luo ◽  
Huai Ying Sun ◽  
De Wang
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Learning Communities ◽  
Information Gain ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Gain Ratio ◽  
Information Gain Ratio ◽  
Improved Algorithm

This paper presents an improved SPRINT algorithm. The original SPRINT algorithm is a scalable and parallelizable decision tree algorithm, which is a popular algorithm in data mining and machine learning communities. To improve the algorithm's efficiency, we propose an improved algorithm. Firstly, we select the splitting attributes and obtain the best splitting attribute from them by computing the information gain ratio of each attribute. After that, we calculate the best splitting point of the best splitting attribute. Since it avoids a lot of calculations of other attributes, the improved algorithm can effectively reduce the computation.

scholarly journals PREDIKSI CALON MAHASISWA BARU MENGUNAKAN METODE KLASIFIKASI DECISION TREE

2015 ◽  
Vol 7 (1) ◽  
pp. 48
Author(s):  
Mambang Mambang ◽  
Finki Dona Marleny
Keyword(s):  
Decision Tree ◽  
Information Gain ◽  
Internal Node ◽  
Terminal Node ◽  
Gain Ratio ◽  
Root Node ◽  
Information Gain Ratio

<p>Sebelum penyelengaraan pendidikan tenaga kesehatan memulai tahun ajaran baru, maka langkah awal akan dilaksanakan seleksi penerimaan mahasiswa baru yang berasal dari lulusan pendidikan menengah umum maupun kejuruan yang sederajat. Seleksi penerimaan mahasiswa baru ini bertujuan untuk menyaring calon mahasiswa dari berbagai latar belakang yang di sesuaikan dengan standar yang telah di tentukan oleh lembaga. Dalam penelitian ini bagaimana akurasi algoritma C4.5 untuk memprediksi kelulusan calon mahasiswa baru. Model decision tree merupakan metode prediksi klasifikasi untuk membuat sebuah tree yang terdiri dari root node, internal node dan terminal node. Berdasarkan hasil eksperimen dan evaluasi yang dilakukan maka dapat disimpulkan bahwa Algoritma C4.5 dengan Uncertainty didapatkan Akurasi 80,39%, Precision 94,44%, Recall 75,00% sedangkan dengan Algoritma C4.5 dengan Information Gain Ratio Akurasi 88,24%, Precision 98,28%, Recall 83,82%. </p>

scholarly journals Landslide susceptibility modelling based on decision tree classification model of machine learning: A case study of Kullu-Rohtang Pass transport corridor (Himachal Pradesh), India

2021 ◽  
Author(s):  
Nirbhav Sharma ◽  
Ram Babu Singh ◽  
Anand Malik ◽  
Maheshwar Sharma
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Landslide Susceptibility ◽  
Information Gain ◽  
Himachal Pradesh ◽  
Classification Model ◽  
Decision Tree Classification ◽  
Landslide Hazards ◽  
Susceptibility Model ◽  
Information Gain Ratio

Abstract Landslide hazards are responsible for causing substantial destruction and losses in mountainous region. In order to lessen the damage in these vulnerable areas, the key challenge is to predict the landslide events with accuracy and precision. The principal objective of the study conducted is to assess the landslide susceptibility along the transport corridor from Kullu to Rohtang Pass in Himachal Pradesh, India. To achieve this objective, a detailed landslide inventory has been prepared based on the imagery data and frequent field visits. A total of 197 landslides were taken under consideration including 153 rock slides and 44 debris slides. Nine landslide factors were prepared initially and their relationships with each other and with the type of landslide was analysed. Later, information gain ratio measure was used to identify the triggering factors having best score for eliminating the unimportant factors. Train_test_split method was used to classify the dataset into training and testing groups. Decision tree classification model of machine learning was applied for landslide susceptibility model (LSM). The performance was evaluated using classification report and receiver operating characteristic (ROC) curve. Results obtained have proved that the decision tree classification model of machine learning performed well and have a good accuracy in forecasting landslide susceptibility in the area considered for this study.

scholarly journals A Numerical Method for Optimization of the Arrangement of Elements of a Video Surveillance System Taking into Account their Safety

2021 ◽  
Vol 27 (1) ◽  
pp. 073-080
Author(s):  
V. V. Menshikh ◽  
◽  
N. E. Chirkova ◽  
Keyword(s):  
Decision Tree ◽  
Numerical Method ◽  
Video Surveillance ◽  
Branch And Bound ◽  
Surveillance System ◽  
High Efficiency ◽  
Optimal Solution ◽  
Video Surveillance System ◽  
Numerical Example

The article deals with the development of a numerical method for optimizing the arrangement of elements of a video surveillance system, taking into account their own safety. The necessity of using the branch and bound scheme in the development of this method is substantiated, which makes it possible to search for an optimal solution with high efficiency. Methods of forming a partial decision tree, evaluating partial solutions, traversing the vertices of a partial decision tree are determined. A numerical example of the implementation of the proposed method is shown.

AFOA: An Adaptive Fruit Fly Optimization Algorithm with Global Optimizing Ability

2016 ◽  
Vol 25 (06) ◽  
pp. 1650032 ◽  
Author(s):  
Yiwen Zhang ◽  
Guangming Cui ◽  
Erzhou Zhu ◽  
Qiang He
Keyword(s):  
Optimization Algorithm ◽  
High Efficiency ◽  
Historical Memory ◽  
Optimal Solution ◽  
Fruit Fly ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Local Optimal Solution ◽  
Evolution Algorithms ◽  
Elitist Strategy

With the development of intelligent computation technology, the intelligent evolution algorithms have been widely applied to solve optimization problem in the real world. As a novel evolution algorithm, fruit fly optimization algorithm (FOA) has the advantages of simple operation and high efficiency. However, FOA also has some disadvantages, such as trapping into local optimal solution easily, failing to traverse the problem domain and limiting the universality. In order to cope with the disadvantages of FOA while retain it merits, this paper proposes AFOA, an adaptive fruit fly optimization algorithm. AFOA adjusts the swarm range parameter V dynamically and adaptively according to the historical memory of each iteration of the swarm, and adopts the more accurate elitist strategy, which is therefore very effective in both accelerating the convergence of the swarm to the global optimal front and maintaining diversity of the solutions. The convergence of the algorithm is firstly analyzed theoretically, and then 14 benchmark functions with different characteristics are executed to compare the performance among AFOA, PSO, FOA, and LGMS-FOA. The experimental results have shown that, AFOA algorithm is a new algorithm with global optimizing capability and high universality.

A Novel Approach using Expert Knowledge on Error based Pruning

2012 ◽  
Vol 11 (01) ◽  
pp. 1250007
Author(s):  
Ali Mirza Mahmood ◽  
Mrithyumjaya Rao Kuppa
Keyword(s):  
Decision Tree ◽  
Real World ◽  
Missing Values ◽  
Expert Knowledge ◽  
Classification Problem ◽  
Misclassification Rate ◽  
Classification Problems ◽  
Knowledge Based ◽  
Novel Approach ◽  
Real World Datasets

Many traditional pruning methods assume that all the datasets are equally probable and equally important, so they apply equal pruning to all the datasets. However, in real-world classification problems, all the datasets are not equal and considering equal pruning rate during pruning tends to generate a decision tree with a large size and high misclassification rate. In this paper, we present a practical algorithm to deal with the data specific classification problem when there are datasets with different properties. Another key motivation of the data specific pruning in the paper is "trading accuracy and size". A new algorithm called Expert Knowledge Based Pruning (EKBP) is proposed to solve this dilemma. We proposed to integrate error rate, missing values and expert judgment as factors for determining data specific pruning for each dataset. We show by analysis and experiments that using this pruning, we can scale both accuracy and generalisation for the tree that is generated. Moreover, the method can be very effective for high dimensional datasets. We conduct an extensive experimental study on openly available 40 real world datasets from UCI repository. In all these experiments, the proposed approach shows considerably reduction of tree size having equal or better accuracy compared to several benchmark decision tree methods that are proposed in literature.

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

scholarly journals A branch & bound algorithm to determine optimal bivariate splits for oblique decision tree induction

2021 ◽  
Author(s):  
Ferdinand Bollwein ◽  
Stephan Westphal
Keyword(s):  
Decision Tree ◽  
Feature Space ◽  
Classification Problems ◽  
Decision Tree Induction ◽  
Single Attribute ◽  
Global Optimal ◽  
The Individual ◽  
Tree Building ◽  
Very High ◽  
Multiclass Classification Problems

AbstractUnivariate decision tree induction methods for multiclass classification problems such as CART, C4.5 and ID3 continue to be very popular in the context of machine learning due to their major benefit of being easy to interpret. However, as these trees only consider a single attribute per node, they often get quite large which lowers their explanatory value. Oblique decision tree building algorithms, which divide the feature space by multidimensional hyperplanes, often produce much smaller trees but the individual splits are hard to interpret. Moreover, the effort of finding optimal oblique splits is very high such that heuristics have to be applied to determine local optimal solutions. In this work, we introduce an effective branch and bound procedure to determine global optimal bivariate oblique splits for concave impurity measures. Decision trees based on these bivariate oblique splits remain fairly interpretable due to the restriction to two attributes per split. The resulting trees are significantly smaller and more accurate than their univariate counterparts due to their ability of adapting better to the underlying data and capturing interactions of attribute pairs. Moreover, our evaluation shows that our algorithm even outperforms algorithms based on heuristically obtained multivariate oblique splits despite the fact that we are focusing on two attributes only.

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