Using Genetic Programming Systems as Early Warning to Prevent Bank Failure

2011 ◽  
pp. 369-382
Author(s):  
Alma Lilia Garcia Almanza ◽  
Serafín Martínez Jaramillo ◽  
Biliana Alexandrova-Kabadjova ◽  
Edward Tsang
Keyword(s):  
Genetic Programming ◽  
Receiver Operating Characteristic ◽  
Early Warning ◽  
Roc Curve ◽  
Bank Failure ◽  
Experimental Results ◽  
Financial Indicators ◽  
Population Approach ◽  
Series Of Experiments ◽  
Programming Systems

The main advantage of creating understandable rules is that users are able to interpret and identify the events that may trigger bankruptcy. By using the method that we propose in this work, it is possible to identify when certain financial indicators are getting close to specific thresholds, something that can turn into an undesirable situation. This is particularly relevant if the companies we are referring to are banks. The contribution of this chapter is to improve the prediction by means of a multi-population approach. The experimental results were evaluated using the Receiver Operating Characteristic (ROC) described in Fawcett and Provost (1997). We show that our approach could improve the Area Under the ROC Curve in 5% with respect to the same method proposed in Garcia et al. (2010). Additionally, a series of experiments were performed in order to find out the reasons of success of the EDR.

scholarly journals An attempt to determine the adiabatic relations of ethyl oxide

1897 ◽  
Vol 60 (359-367) ◽  
pp. 336-337 ◽  
Keyword(s):  
Wave Length ◽  
Experimental Results ◽  
Mathematical Discussion ◽  
Series Of Experiments

The wave-length of sound in gaseous and in liquid ethyl oxide (sulphuric ether) has been determined by the two first-mentioned of the authors, by means of Kundt’s method, between limits of temperature ranging from 100° C. to 200° C., and of pressure ranging from 4000 mm. to 31,000 mm. of mercury, and of volume ranging from 2·6 c. c, per gram to 71 c. c. per gram. Making use of the same apparatus throughout, the results obtained are to be regarded as comparative, and, by careful determination of the pitch of the tone transmitted through the gas, it is probable they are approximately absolute. The sections of the complete memoir deal with (I) a description of the apparatus employed, (II) the method of ascertaining the weights of ether used in each series of experiments, (III) determinations of the frequency of the vibrating rod, (IV) the calculations of the adiabatic elasticity and tables of the experimental results, and (V) a mathematical discussion of the results. The last section is due to Mr. Rose-Innes.

scholarly journals Parsing Expression Grammars and Their Induction Algorithm

2020 ◽  
Vol 10 (23) ◽  
pp. 8747
Author(s):  
Wojciech Wieczorek ◽  
Olgierd Unold ◽  
Łukasz Strąk
Keyword(s):  
Genetic Programming ◽  
Roc Curve ◽  
Classification Performance ◽  
Grammatical Inference ◽  
Inference Algorithm ◽  
Finite Sample ◽  
Induction Method ◽  
Grammar Learning ◽  
Area Under Roc Curve ◽  
Inference Methods

Grammatical inference (GI), i.e., the task of finding a rule that lies behind given words, can be used in the analyses of amyloidogenic sequence fragments, which are essential in studies of neurodegenerative diseases. In this paper, we developed a new method that generates non-circular parsing expression grammars (PEGs) and compares it with other GI algorithms on the sequences from a real dataset. The main contribution of this paper is a genetic programming-based algorithm for the induction of parsing expression grammars from a finite sample. The induction method has been tested on a real bioinformatics dataset and its classification performance has been compared to the achievements of existing grammatical inference methods. The evaluation of the generated PEG on an amyloidogenic dataset revealed its accuracy when predicting amyloid segments. We show that the new grammatical inference algorithm achieves the best ACC (Accuracy), AUC (Area under ROC curve), and MCC (Mathew’s correlation coefficient) scores in comparison to five other automata or grammar learning methods.

scholarly journals Evaluation of Ocular Biomechanical Indices to Distinguish Normal from Keratoconus Eyes

2012 ◽  
Vol 1 (3) ◽  
pp. 145-150
Author(s):  
Bernardo Lopes ◽  
Allan Luz ◽  
Bruno Fontes ◽  
Isaac C Ramos ◽  
Fernando Correia ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Receiver Operating Characteristic ◽  
Linear Model ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Predictive Accuracy ◽  
Corneal Resistance Factor ◽  
Corneal Deformation ◽  
Eye Examination ◽  
The Individual

ABSTRACT Purpose To compare and assess the ability of pressure-derived parameters and corneal deformation waveform signal-derived parameters of the ocular response analyzer (ORA) measurement to distinguish between keratoconus and normal eyes, and to develop a combined parameter to optimize the diagnosis of keratoconus. Materials and methods One hundred and seventy-seven eyes (177 patients) with keratoconus (group KC) and 205 normal eyes (205 patients; group N) were included. One eye from each subject was randomly selected for analysis. Patients underwent a complete clinical eye examination, corneal topography (Humphrey ATLAS), tomography (Pentacam Oculus) and biomechanical evaluations (ORA Reichert). Differences in the distributions between the groups were assessed using the Mann- Whitney test. The receiver operating characteristic (ROC) curve was used to identify cutoff points that maximized sensitivity and specificity in discriminating keratoconus from normal corneas. Logistic regression was used to identify a combined linear model (Fisher 1.0). Results Significant differences in all studied parameters were detected (p < 0.05), except for W2. For the corneal resistance factor (CRF): Area under the ROC curve (AUROC) 89.1%, sensitivity 81.36%, specificity 84.88%. For the p1area: AUROC 91.5%, sensitivity 87.1%, specificity 81.95%. Of the individual parameters, the highest predictive accuracy was for the Fisher 1.0, which represents the combination of all parameters (AUROC 95.5%, sensitivity 88.14%, specificity 93.17%). Conclusion Waveform-derived ORA parameters displayed greater accuracy than pressure-derived parameters for identifying keratoconus. Corneal hysteresis (CH) and CRF, a diagnostic linear model that combines different parameters, provided the greatest accuracy for differentiating keratoconus from normal corneas. How to cite this article Luz A, Fontes B, Ramos IC, Lopes B, Correia F, Schor P, Ambrósio R. Evaluation of Ocular Biomechanical Indices to Distinguish Normal from Keratoconus Eyes. Int J Kerat Ect Cor Dis 2012;1(3):145-150.

scholarly journals Exploring genetic programming systems with MAP-Elites

2018 ◽  
Author(s):  
Emily Dolson ◽  
Alexander Lalejini ◽  
Charles Ofria
Keyword(s):  
Neural Networks ◽  
Genetic Programming ◽  
Pareto Front ◽  
Underlying Structure ◽  
Phenotypic Traits ◽  
Computation Technique ◽  
Phenotype Space ◽  
Programming Systems ◽  
Insight Into ◽  
Connection Cost

MAP-Elites is an evolutionary computation technique that has proven valuable for exploring and illuminating the genotype-phenotype space of a computational problem. In MAP-Elites, a population is structured based on phenotypic traits of prospective solutions; each cell represents a distinct combination of traits and maintains only the most fit organism found with those traits. The resulting map of trait combinations allows the user to develop a better understanding of how each trait relates to fitness and how traits interact. While MAP-Elites has not been demonstrated to be competitive for identifying the optimal Pareto front, the insights it provides do allow users to better understand the underlying problem. In particular, MAP-Elites has provided insight into the underlying structure of problem representations, such as the value of connection cost or modularity to evolving neural networks. Here, we extend the use of MAP-Elites to examine genetic programming representations, using aspects of program architecture as traits to explore. We demonstrate that MAP-Elites can generate programs with a much wider range of architectures than other evolutionary algorithms do (even those that are highly successful at maintaining diversity), which is not surprising as this is the purpose of MAP-Elites. Ultimately, we propose that MAP-Elites is a useful tool for understanding why genetic programming representations succeed or fail and we suggest that it should be used to choose selection techniques and tune parameters.

Grammar Design for Derivation Tree Based Genetic Programming Systems

2016 ◽  
pp. 199-214 ◽  
Author(s):  
Stefan Forstenlechner ◽  
Miguel Nicolau ◽  
David Fagan ◽  
Michael O’Neill
Keyword(s):  
Genetic Programming ◽  
Derivation Tree ◽  
Programming Systems
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