scholarly journals Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm

1995 ◽  
Vol 2 ◽  
pp. 369-409 ◽  
Author(s):  
P. D. Turney
Keyword(s):  
Genetic Algorithm ◽  
Decision Tree ◽  
Real World ◽  
Average Cost ◽  
Fitness Function ◽  
Empirical Evaluation ◽  
The Third ◽  
Decision Tree Induction ◽  
Cost Sensitive Classification ◽  
Better Than

This paper introduces ICET, a new algorithm for cost-sensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors. ICET is compared here with three other algorithms for cost-sensitive classification - EG2, CS-ID3, and IDX - and also with C4.5, which classifies without regard to cost. The five algorithms are evaluated empirically on five real-world medical datasets. Three sets of experiments are performed. The first set examines the baseline performance of the five algorithms on the five datasets and establishes that ICET performs significantly better than its competitors. The second set tests the robustness of ICET under a variety of conditions and shows that ICET maintains its advantage. The third set looks at ICET's search in bias space and discovers a way to improve the search.

Newton’s Law of Universal Gravitation Based Genetic Algorithm for Graph Distribution

2016 ◽  
Vol 15 (04) ◽  
pp. 1650022
Author(s):  
Tabib Nacer ◽  
Saidouni Djamel Eddine
Keyword(s):  
Genetic Algorithm ◽  
Comparative Studies ◽  
Fitness Function ◽  
The Novel ◽  
Distribution Problem ◽  
Universal Gravitation ◽  
Workload Balancing ◽  
The Third ◽  
Heterogeneous Architectures ◽  
The Impact

This paper proposes a novel versatile genetic algorithm (GA) for solving the graph distribution problem. The new GA is based mainly on an inspirational idea that exploits the Newton’s universal gravitation law to introduce a novel GA fitness function. By this,the new GA preserves the workload balancing property between the different sites of the graph network and reduces the inter-processors communications overhead. Moreover, three main variants of the novel GA are developed. The two first; centralized and distributed variants, are developed to conduct a graph distribution over homogeneous architectures. The third variant is a distributed one devoted for heterogeneous architectures where the impact of the auto-adaptation features of the GA emerges. The results obtained and the comparative studies show the effectiveness of the proposed methods.

scholarly journals Load Balancing Selection Method and Simulation in Network Communication Based on AHP-DS Heterogeneous Network Selection Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Weiwei Xiao
Keyword(s):  
Genetic Algorithm ◽  
Decision Tree ◽  
Service Quality ◽  
Balancing Selection ◽  
Fitness Function ◽  
Training Data ◽  
Network Selection ◽  
Network Access ◽  
Selection Algorithm ◽  
Decision Algorithm

This article proposes an Analytic Hierarchy Process Dempster-Shafer (AHP-DS) and similarity-based network selection algorithm for the scenario of dynamic changes in user requirements and network environment; combines machine learning with network selection and proposes a decision tree-based network selection algorithm; combines multiattribute decision-making and genetic algorithm to propose a weighted Gray Relation Analysis (GRA) and genetic algorithm-based network access decision algorithm. Firstly, the training data is obtained from the collaborative algorithm, and it is used as the training set, and the network attributes are used as the attribute set, and the continuous attributes are discretized by dichotomization, and the attribute that can make the greatest information gain is selected as the division feature, and a decision tree with strong generalization ability is finally obtained, which is used as the decision basis for network access selection. The simulation results show that the algorithm proposed in this thesis can effectively improve user service quality under three services, and the algorithm is simple and effective with low complexity. It first uses AHP-DS hierarchical analysis to establish a recursive hierarchy for the network selection problem and obtains the subjective weights of network attributes through the judgment matrix. Then, it uses a genetic algorithm to adjust the subjective weight, defines the fitness function in the genetic algorithm-based on gray correlation analysis, adjusts the weights of the selection operator, crossover operator, and variation operator in the genetic algorithm, and gets the network with the largest fitness as the target network, which can effectively improve the user service quality.

scholarly journals Using Formal Grammars as Musical Genome

2021 ◽  
Vol 11 (9) ◽  
pp. 4151
Author(s):  
David D. Albarracín-Molina ◽  
Alfredo Raglio ◽  
Francisco Rivas-Ruiz ◽  
Francisco J. Vico
Keyword(s):  
Genetic Algorithm ◽  
Real World ◽  
Fitness Function ◽  
Tonal Music ◽  
Formal Grammars ◽  
Generative Music ◽  
Engineering Problems ◽  
Representation Method ◽  
Basic Features ◽  
Program Interface

In this paper, we explore a generative music method that can compose atonal and tonal music in different styles. One of the main differences between regular engineering problems and artistic expressions is that goals and constraints are usually ill-defined in the latter case; in fact the rules here could or should be transgressed more regularly. For this reason, our approach does not use a pre-existing dataset to imitate or extract rules from. Instead, it uses formal grammars as a representation method than can retain just the basic features, common to any form of music (e.g., the appearance of rhythmic patterns, the evolution of tone or dynamics during the composition, etc.). Exploring different musical spaces is the responsibility of a program interface that translates musical specifications into the fitness function of a genetic algorithm. This function guides the evolution of those basic features enabling the emergence of novel content. In this study, we then assess the outcome of a particular music specification (guitar ballad) in a controlled real-world setup. As a result, the generated music can be considered similar to human-composed music from a perceptual perspective. This endorses our approach to tackle arts algorithmically, as it is able to produce novel content that complies with human expectations.

Implicit Representation in Genetic Algorithms Using Redundancy

1997 ◽  
Vol 5 (3) ◽  
pp. 277-302 ◽  
Author(s):  
Anne M. Raich ◽  
Jamshid Ghaboussi
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Search Space ◽  
Function Evaluation ◽  
Diverse Population ◽  
Implicit Representation ◽  
Essential Information ◽  
Redundant Representation ◽  
Better Than

A new representation combining redundancy and implicit fitness constraints is introduced that performs better than a simple genetic algorithm (GA) and a structured GA in experiments. The implicit redundant representation (IRR) consists of a string that is over-specified, allowing for sections of the string to remain inactive during function evaluation. The representation does not require the user to prespecify the number of parameters to evaluate or the location of these parameters within the string. This information is obtained implicitly by the fitness function during the GA operations. The good performance of the IRR can be attributed to several factors: less disruption of existing fit members due to the increased probability of crossovers and mutation affecting only redundant material; discovery of fit members through the conversion of redundant material into essential information; and the ability to enlarge or reduce the search space dynamically by varying the number of variables evaluated by the fitness function. The IRR GA provides a more biologically parallel representation that maintains a diverse population throughout the evolution process. In addition, the IRR provides the necessary flexibility to represent unstructured problem domains that do not have the explicit constraints required by fixed representations.

A Developmental Study of Intonation Recognition

1968 ◽  
Vol 11 (4) ◽  
pp. 825-832 ◽  
Author(s):  
Marilyn M. Corlew
Keyword(s):  
Significant Interaction ◽  
Multiple Choice ◽  
Semantic Content ◽  
First Grade ◽  
Choice Test ◽  
Developmental Study ◽  
Multiple Choice Test ◽  
The Third ◽  
Matching Test ◽  
Better Than

Two experiments investigated the information conveyed by intonation from speaker to listener. A multiple-choice test was devised to test the ability of 48 adults to recognize and label intonation when it was separated from all other meaning. Nine intonation contours whose labels were most agreed upon by adults were each matched with two English sentences (one with appropriate and one with inappropriate intonation and semantic content) to make a matching-test for children. The matching-test was tape-recorded and given to children in the first, third, and fifth grades (32 subjects in each grade). The first-grade children matched the intonations with significantly greater agreement than chance; but they agreed upon significantly fewer sentences than either the third or fifth graders. Some intonation contours were matched with significantly greater frequency than others. The performance of the girls was better than that of the boys on an impatient question and a simple command which indicates that there was a significant interaction between sex and intonation.

Using Connectionist Modules for Decision Support

1990 ◽  
Vol 29 (03) ◽  
pp. 167-181 ◽  
Author(s):  
G. Hripcsak
Keyword(s):  
Decision Support ◽  
Standard Deviation ◽  
Confidence Interval ◽  
Posterior Probability ◽  
Back Propagation ◽  
Connectionist Model ◽  
Test Set ◽  
The Third ◽  
Independent Test ◽  
Better Than

AbstractA connectionist model for decision support was constructed out of several back-propagation modules. Manifestations serve as input to the model; they may be real-valued, and the confidence in their measurement may be specified. The model produces as its output the posterior probability of disease. The model was trained on 1,000 cases taken from a simulated underlying population with three conditionally independent manifestations. The first manifestation had a linear relationship between value and posterior probability of disease, the second had a stepped relationship, and the third was normally distributed. An independent test set of 30,000 cases showed that the model was better able to estimate the posterior probability of disease (the standard deviation of residuals was 0.046, with a 95% confidence interval of 0.046-0.047) than a model constructed using logistic regression (with a standard deviation of residuals of 0.062, with a 95% confidence interval of 0.062-0.063). The model fitted the normal and stepped manifestations better than the linear one. It accommodated intermediate levels of confidence well.

scholarly journals Network robustness improvement via long-range links

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Vincenza Carchiolo ◽  
Marco Grassia ◽  
Alessandro Longheu ◽  
Michele Malgeri ◽  
Giuseppe Mangioni
Keyword(s):  
Complex Networks ◽  
Long Range ◽  
Real World ◽  
Network Robustness ◽  
Scale Free ◽  
Area Of Interest ◽  
Before And After ◽  
Significant Area ◽  
Better Than

AbstractMany systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks robustness, which aims to explore to what extent a network keeps working when failures occur in its structure and how disruptions can be avoided. In this paper, we introduce the idea of exploiting long-range links to improve the robustness of Scale-Free (SF) networks. Several experiments are carried out by attacking the networks before and after the addition of links between the farthest nodes, and the results show that this approach effectively improves the SF network correct functionalities better than other commonly used strategies.

scholarly journals Deep learning for intelligent diagnosis in thyroid scintigraphy

2021 ◽  
Vol 49 (1) ◽  
pp. 030006052098284
Author(s):  
Tingting Qiao ◽  
Simin Liu ◽  
Zhijun Cui ◽  
Xiaqing Yu ◽  
Haidong Cai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Disease Diagnosis ◽  
Kappa Coefficient ◽  
Diagnostic Assessment ◽  
First Year ◽  
Thyroid Scintigraphy ◽  
Diagnostic Ability ◽  
The Third ◽  
Classification Time ◽  
Better Than

Objective To construct deep learning (DL) models to improve the accuracy and efficiency of thyroid disease diagnosis by thyroid scintigraphy. Methods We constructed DL models with AlexNet, VGGNet, and ResNet. The models were trained separately with transfer learning. We measured each model’s performance with six indicators: recall, precision, negative predictive value (NPV), specificity, accuracy, and F1-score. We also compared the diagnostic performances of first- and third-year nuclear medicine (NM) residents with assistance from the best-performing DL-based model. The Kappa coefficient and average classification time of each model were compared with those of two NM residents. Results The recall, precision, NPV, specificity, accuracy, and F1-score of the three models ranged from 73.33% to 97.00%. The Kappa coefficient of all three models was >0.710. All models performed better than the first-year NM resident but not as well as the third-year NM resident in terms of diagnostic ability. However, the ResNet model provided “diagnostic assistance” to the NM residents. The models provided results at speeds 400 to 600 times faster than the NM residents. Conclusion DL-based models perform well in diagnostic assessment by thyroid scintigraphy. These models may serve as tools for NM residents in the diagnosis of Graves’ disease and subacute thyroiditis.

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