Multi-Attribution Classification Based on Lower Integral with Genetic Algorithm

2014 ◽  
Vol 668-669 ◽  
pp. 1090-1093
Author(s):  
Ai Xia Chen ◽  
Jun Hua Li
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Linear Programming Problem ◽  
Fuzzy Integral ◽  
Data Sets ◽  
Fuzzy Measures ◽  
Benchmark Data ◽  
Minimum Potential ◽  
And Performance ◽  
Set Function

Fuzzy integral has been widely used in multi-attribution classification when the interactions exist between the attributions. Because the fuzzy measure defined on the attributions represents the weights of all the attributions and the interactions between them. The lower integral is a type of fuzzy integral with respect to fuzzy measures, which represents the minimum potential of efficiency for a group of attributions with interaction. The value of lower integrals can be evaluated through solving a linear programming problem. Considering the lower integral as a classifier, this paper investigates its implementation and performance. The difficult step in the implementation is how to learn the non-additive set function used in lower integrals. And Genetic algorithm is used to solve the problem. Finally, numerical simulations on some benchmark data sets are given.

Multi-Frequency Matched-Field Inversion of Benchmark Data Using a Genetic Algorithm

1998 ◽  
Vol 06 (01n02) ◽  
pp. 135-150 ◽  
Author(s):  
D. G. Simons ◽  
M. Snellen
Keyword(s):  
Genetic Algorithm ◽  
Test Cases ◽  
Data Sets ◽  
True Parameter ◽  
Benchmark Data ◽  
Line Array ◽  
Standard Normal ◽  
Water Test ◽  
Parameter Values ◽  
Field Inversion

For a selected number of shallow water test cases of the 1997 Geoacoustic Inversion Workshop we have applied Matched-Field Inversion to determine the geoacoustic and geometric (source location, water depth) parameters. A genetic algorithm has been applied for performing the optimization, whereas the replica fields have been calculated using a standard normal-mode model. The energy function to be optimized is based on the incoherent multi-frequency Bartlett processor. We have used the data sets provided at a few frequencies in the band 25–500 Hz for a vertical line array positioned at 5 km from the source. A comparison between the inverted and true parameter values is made.

Improving Effectiveness of Intrusion Detection by Correlation Feature Selection

2012 ◽  
pp. 22-35
Author(s):  
Hai Thanh Nguyen ◽  
Katrin Franke ◽  
Slobodan Petrovic
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Linear Programming ◽  
Intrusion Detection ◽  
Programming Problem ◽  
Linear Programming Problem ◽  
Selection Procedure ◽  
Filter Method ◽  
New Feature ◽  
Kdd Cup 99

In this paper, the authors propose a new feature selection procedure for intrusion detection, which is based on filter method used in machine learning. They focus on Correlation Feature Selection (CFS) and transform the problem of feature selection by means of CFS measure into a mixed 0-1 linear programming problem with a number of constraints and variables that is linear in the number of full set features. The mixed 0-1 linear programming problem can then be solved by using branch-and-bound algorithm. This feature selection algorithm was compared experimentally with the best-first-CFS and the genetic-algorithm-CFS methods regarding the feature selection capabilities. Classification accuracies obtained after the feature selection by means of the C4.5 and the BayesNet over the KDD CUP’99 dataset were also tested. Experiments show that the authors’ method outperforms the best-first-CFS and the genetic-algorithm-CFS methods by removing much more redundant features while keeping the classification accuracies or getting better performances.

Improving Effectiveness of Intrusion Detection by Correlation Feature Selection

2011 ◽  
Vol 3 (1) ◽  
pp. 21-34 ◽  
Author(s):  
Hai Thanh Nguyen ◽  
Katrin Franke ◽  
Slobodan Petrovic
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Linear Programming ◽  
Intrusion Detection ◽  
Programming Problem ◽  
Linear Programming Problem ◽  
Selection Procedure ◽  
Filter Method ◽  
Selection Algorithm ◽  
New Feature

In this paper, the authors propose a new feature selection procedure for intrusion detection, which is based on filter method used in machine learning. They focus on Correlation Feature Selection (CFS) and transform the problem of feature selection by means of CFS measure into a mixed 0-1 linear programming problem with a number of constraints and variables that is linear in the number of full set features. The mixed 0-1 linear programming problem can then be solved by using branch-and-bound algorithm. This feature selection algorithm was compared experimentally with the best-first-CFS and the genetic-algorithm-CFS methods regarding the feature selection capabilities. Classification accuracies obtained after the feature selection by means of the C4.5 and the BayesNet over the KDD CUP’99 dataset were also tested. Experiments show that the authors’ method outperforms the best-first-CFS and the genetic-algorithm-CFS methods by removing much more redundant features while keeping the classification accuracies or getting better performances.

Agricultural Robotics

2019 ◽  
Vol 2 (1) ◽  
pp. 365-392 ◽  
Author(s):  
Stavros G. Vougioukas
Keyword(s):  
Data Driven ◽  
Farm Labor ◽  
Data Sets ◽  
Future Directions ◽  
Agricultural Robots ◽  
Benchmark Data ◽  
Labor Inputs ◽  
Sustainable Fashion ◽  
And Performance ◽  
Physical And Chemical

A key goal of contemporary agriculture is to dramatically increase production of food, feed, fiber, and biofuel products in a sustainable fashion, facing the pressure of diminishing farm labor supply. Agricultural robots can accelerate plant breeding and advance data-driven precision farming with significantly reduced labor inputs by providing task-appropriate sensing and actuation at fine spatiotemporal resolutions. This article highlights the distinctive challenges imposed on ground robots by agricultural environments, which are characterized by wide variations in environmental conditions, diversity and complexity of plant canopy structures, and intraspecies biological variation of physical and chemical characteristics and responses to the environment. Existing approaches to address these challenges are presented, along with their limitations; possible future directions are also discussed. Two key observations are that biology (breeding) and horticultural practices can reduce variabilities at the source and that publicly available benchmark data sets are needed to increase perception robustness and performance despite variability.

scholarly journals Completely Automated CNN Architecture Design Based on Blocks

2020 ◽  
Author(s):  
Y Sun ◽  
Bing Xue ◽  
Mengjie Zhang ◽  
GG Yen
Keyword(s):  
Genetic Algorithm ◽  
Classification Accuracy ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Classification Performance ◽  
Data Sets ◽  
Computational Resource ◽  
Problem Domain ◽  
Benchmark Data ◽  
Interested User

© 2019 IEEE. The performance of convolutional neural networks (CNNs) highly relies on their architectures. In order to design a CNN with promising performance, extensive expertise in both CNNs and the investigated problem domain is required, which is not necessarily available to every interested user. To address this problem, we propose to automatically evolve CNN architectures by using a genetic algorithm (GA) based on ResNet and DenseNet blocks. The proposed algorithm is completely automatic in designing CNN architectures. In particular, neither preprocessing before it starts nor postprocessing in terms of CNNs is needed. Furthermore, the proposed algorithm does not require users with domain knowledge on CNNs, the investigated problem, or even GAs. The proposed algorithm is evaluated on the CIFAR10 and CIFAR100 benchmark data sets against 18 state-of-the-art peer competitors. Experimental results show that the proposed algorithm outperforms the state-of-the-art CNNs hand-crafted and the CNNs designed by automatic peer competitors in terms of the classification performance and achieves a competitive classification accuracy against semiautomatic peer competitors. In addition, the proposed algorithm consumes much less computational resource than most peer competitors in finding the best CNN architectures.

scholarly journals Completely Automated CNN Architecture Design Based on Blocks

2020 ◽  
Author(s):  
Y Sun ◽  
Bing Xue ◽  
Mengjie Zhang ◽  
GG Yen
Keyword(s):  
Genetic Algorithm ◽  
Classification Accuracy ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Classification Performance ◽  
Data Sets ◽  
Computational Resource ◽  
Problem Domain ◽  
Benchmark Data ◽  
Interested User

© 2019 IEEE. The performance of convolutional neural networks (CNNs) highly relies on their architectures. In order to design a CNN with promising performance, extensive expertise in both CNNs and the investigated problem domain is required, which is not necessarily available to every interested user. To address this problem, we propose to automatically evolve CNN architectures by using a genetic algorithm (GA) based on ResNet and DenseNet blocks. The proposed algorithm is completely automatic in designing CNN architectures. In particular, neither preprocessing before it starts nor postprocessing in terms of CNNs is needed. Furthermore, the proposed algorithm does not require users with domain knowledge on CNNs, the investigated problem, or even GAs. The proposed algorithm is evaluated on the CIFAR10 and CIFAR100 benchmark data sets against 18 state-of-the-art peer competitors. Experimental results show that the proposed algorithm outperforms the state-of-the-art CNNs hand-crafted and the CNNs designed by automatic peer competitors in terms of the classification performance and achieves a competitive classification accuracy against semiautomatic peer competitors. In addition, the proposed algorithm consumes much less computational resource than most peer competitors in finding the best CNN architectures.

Transportation Cloud Service Composition Based on Fuzzy Programming and Genetic Algorithm

2018 ◽  
Vol 2672 (45) ◽  
pp. 64-75
Author(s):  
Weibin Zhang ◽  
Haifeng Guo ◽  
Ziqiang Zeng ◽  
Yong Qi ◽  
Yinhai Wang
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Web Service ◽  
Service Composition ◽  
Big Data Analytics ◽  
Fuzzy Programming ◽  
Cloud Service ◽  
Multiple Objective Linear Programming ◽  
And Performance ◽  
Cloud Service Composition

The push toward smarter transportation management and decision-making has increased significantly in recent years. Toward this end, it is constructive to establish a traffic intelligence platform leveraging web services, big data analytics, and cloud computing. The developers of any such platform always face the challenge of selecting or creating composition plans from among numerous possible plans, often with unclear requirements, that satisfy their quality-of-service (QoS) requirements. Typical QoS properties associated with a web service are execution cost and time, availability, successful evaluation, usage frequency, and accuracy. Most of these factors are vague and/or difficult to quantity. To address the need to handle vague inputs, a constraint satisfaction-based web service composition algorithm is proposed, which is based on fuzzy multi-objective linear programming. A genetic algorithm with Pareto optimization evaluation with weighted standardized Euclidean distance is used that demonstrates good performance as the quantity of service candidate increases. The algorithm is capable of finding a satisfactory solution under input of vague QoS requirements, which shows better capability than multiple-objective linear programming. The applicability and performance of the proposed algorithm are validated in an online transportation analytics platform environment.

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