80,113 research outputs found
Evolutionary Algorithms in Decision Tree Induction
One of the biggest problem that many data analysis techniques have to deal with nowadays is Combinatorial Optimization that, in the past, has led many methods to be taken apart. Actually, the (still not enough!) higher computing power available makes it possible to apply such techniques within certain bounds. Since other research fields like Artificial Intelligence have been (and still are) dealing with such problems, their contribute to
statistics has been very significant.
This chapter tries to cast the Combinatorial Optimization methods into the Artificial Intelligence framework, particularly with respect Decision Tree Induction, which is considered a powerful instrument for the knowledge extraction and the decision making support. When the exhaustive enumeration and evaluation of all the possible candidate solution to a Tree-based Induction problem is not computationally affordable, the use of Nature Inspired Optimization Algorithms, which have been proven to be powerful instruments for attacking many combinatorial optimization problems, can be of great help.
In this respect, the attention is focused on three main problems involving Decision Tree Induction by mainly focusing the attention on the Classification and Regression Tree-CART (Breiman et al., 1984) algorithm. First, the problem of splitting complex predictors such a multi-attribute ones is faced through the use of Genetic Algorithms. In addition, the possibility of growing “optimal” exploratory trees is also investigated by making use of Ant Colony Optimization (ACO) algorithm. Finally, the derivation of a subset of decision trees for modelling multi-attribute response on the basis of a data-driven heuristic is also described. The proposed approaches might be useful for knowledge extraction from large databases as well as for data mining applications. The solution they offer for complicated data modelling and data analysis problems might be considered for a possible implementation in a Decision Support System (DSS).
The remainder of the chapter is as follows. Section 2 describes the main features and the recent developments of Decision Tree Induction. An overview of Combinatorial Optimization with a particular focus on Genetic Algorithms and Ant Colony Optimization is presented in section 3. The use of these two algorithms within the Decision Tree Induction Framework is described in section 4, together with the description of the algorithm for modelling multi-attribute response. Section 5 summarizes the results of the proposed method on real and simulated datasets. Concluding remarks are presented in section 6. The chapter also includes an appendix that presents J-Fast, a Java-based software for Decision Tree that currently implements Genetic Algorithms and Ant Colony Optimization
Fault Diagnosis Algorithm Based on Power Outage Data in Power Grid
INTRODUCTION: With the rapid development of the power industry, the power system has become more and more complex and prone to failures, which seriously impacts power supply and safety.
OBJECTIVES: Development of efficient and accurate fault diagnosis algorithms for power systems.
METHODS:Proposes a fault diagnosis algorithm based on outage data to construct an outage fault prediction model using accurate data. First, the outage data are collected, pre-processed, feature extracted and reduced to obtain a more efficient data set. Then, an optimized fault diagnosis algorithm is designed based on logit, support vector machine (SVM) and decision tree (DT) to improve the accuracy and efficiency of fault diagnosis.
RESULTS: The method is applied to the natural power system, and the results show that the optimization algorithm outperforms the traditional methods. Specifically, the accuracy of the optimization algorithm can reach 100%, while the accuracy of the traditional logit algorithm and SVM algorithm is only 84% and 93%, which is a significant improvement in the model prediction performance.
CONCLUSION: The author can significantly optimize the performance of its model and construct an outage data mining algorithm with a good predictive ability to achieve grid fault research and judgment, which has a specific application value in the practical field
A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency
In this paper, we address the problem of asset performance monitoring, with the intention
of both detecting any potential reliability problem and predicting any loss of energy consumption
e ciency. This is an important concern for many industries and utilities with very intensive
capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an
approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically
with Association Rule (AR) Mining. The combination of these two techniques can now be done
using software which can handle large volumes of data (big data), but the process still needs to
ensure that the required amount of data will be available during the assets’ life cycle and that its
quality is acceptable. The combination of these two techniques in the proposed sequence di ers
from previous works found in the literature, giving researchers new options to face the problem.
Practical implementation of the proposed approach may lead to novel predictive maintenance models
(emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of
performance and help manage assets’ O&M accordingly. The approach is illustrated using specific
examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-
Decision system based on neural networks to optimize the energy efficiency of a petrochemical plant
The energy efficiency of industrial plants is an important issue in any type of business but particularly in
the chemical industry. Not only is it important in order to reduce costs, but also it is necessary even more
as a means of reducing the amount of fuel that gets wasted, thereby improving productivity, ensuring
better product quality, and generally increasing profits. This article describes a decision system developed
for optimizing the energy efficiency of a petrochemical plant. The system has been developed after
a data mining process of the parameters registered in the past. The designed system carries out an optimization
process of the energy efficiency of the plant based on a combined algorithm that uses the following
for obtaining a solution: On the one hand, the energy efficiency of the operation points occurred in
the past and, on the other hand, a module of two neural networks to obtain new interpolated operation
points. Besides, the work includes a previous discriminant analysis of the variables of the plant in order to
select the parameters most important in the plant and to study the behavior of the energy efficiency
index. This study also helped ensure an optimal training of the neural networks. The robustness of the
system as well as its satisfactory results in the testing process (an average rise in the energy efficiency
of around 7%, reaching, in some cases, up to 45%) have encouraged a consulting company (ALIATIS) to
implement and to integrate the decision system as a pilot software in an SCADA
Application of artificial neural network in market segmentation: A review on recent trends
Despite the significance of Artificial Neural Network (ANN) algorithm to
market segmentation, there is a need of a comprehensive literature review and a
classification system for it towards identification of future trend of market
segmentation research. The present work is the first identifiable academic
literature review of the application of neural network based techniques to
segmentation. Our study has provided an academic database of literature between
the periods of 2000-2010 and proposed a classification scheme for the articles.
One thousands (1000) articles have been identified, and around 100 relevant
selected articles have been subsequently reviewed and classified based on the
major focus of each paper. Findings of this study indicated that the research
area of ANN based applications are receiving most research attention and self
organizing map based applications are second in position to be used in
segmentation. The commonly used models for market segmentation are data mining,
intelligent system etc. Our analysis furnishes a roadmap to guide future
research and aid knowledge accretion and establishment pertaining to the
application of ANN based techniques in market segmentation. Thus the present
work will significantly contribute to both the industry and academic research
in business and marketing as a sustainable valuable knowledge source of market
segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table
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