Using Machine Learning, Neural Networks, and Statistics to Predict Corporate Bankruptcy

Recent literature strongly suggests that machine learning approaches to classification outperform "classical" statistical methods. We make a comparison between the performance of linear discriminant analysis, classification trees, and neural networks in predicting corporate bankruptcy. Linear discriminant analysis represents the "classical" statistical approach to classification, whereas classification trees and neural networks represent artificial intelligence approaches. A proper statistical design is used to be able to test whether observed differences in predictive performance are statistically significant. The data set consists of a collection of 576 annual reports from Belgian construction companies. We use stratified 10–fold cross–validation on the training set to choose "good" parameter values for the different learning methods. The test set is used to obtain an unbiased estimate of the true prediction error. Using rigorous statistical testing, we cannot conclude that in the case of the data set studied, one learning method clearly outperforms the other methods

Neural network ensembles: Evaluation of aggregation algorithms

Ensembles of artificial neural networks show improved generalization capabilities that outperform those of single networks. However, for aggregation to be effective, the individual networks must be as accurate and diverse as possible. An important problem is, then, how to tune the aggregate members in order to have an optimal compromise between these two conflicting conditions. We present here an extensive evaluation of several algorithms for ensemble construction, including new proposals and comparing them with standard methods in the literature. We also discuss a potential problem with sequential aggregation algorithms: the non-frequent but damaging selection through their heuristics of particularly bad ensemble members. We introduce modified algorithms that cope with this problem by allowing individual weighting of aggregate members. Our algorithms and their weighted modifications are favorably tested against other methods in the literature, producing a sensible improvement in performance on most of the standard statistical databases used as benchmarks.Comment: 35 pages, 2 figures, In press AI Journa

Intelligent effective management system of biotechnical objects based on natural disturbances prediction

This article analyses Biotechnical objects as complex processes and external disturbances on them; promising areas of management systems of biotechnical objects development are identified; methodological bases for specialized algorithmic-mathematical software construction based on the methods of game theory and statistical solutions, neural networks (including genetic algorithm), filtering the noise components of information signals are synthesized and tested; variety of architectures of intelligent effective management systems of biotechnical objects are developed and tested

Fuzzy neural networks with genetic algorithm-based learning method

This thesis is on the reasoning of artificial neural networks based on granules for both crisp and uncertain data. However, understanding the data in this way is difficult when the data is so complex. Reducing the complexity of the problems that these networks are attempting to learn as well as decreasing the cost of the learning processes are desired for a better prediction. A suitable prediction in artificial neural networks depends on an in-depth understanding of data and fine tracking of relations between data points. Inaccuracies of the prediction are caused by complexity of data set and the complexity is caused by uncertainty and quantity of data. Uncertainties can be represented in granules, and the reasoning based on granules is known as granular computing. This thesis proposed an improvement of granular neural networks to reach an outcome from uncertain and crisp data. Two methods based on genetic algorithms (GAs) are proposed. Firstly, GA-based fuzzy granular neural networks are improved by GA-based fuzzy artificial neural networks. They consist of two parts: granulation using fuzzy c-mean clustering (FCM), and reasoning by GAbased fuzzy artificial neural networks. In order to extract granular rules, a granulation method is proposed. The method has three stages: construction of all possible granular rules, pruning the repetition, and crossing out granular rules. Secondly, the two-phase GA-based fuzzy artificial neural networks are improved by GA-based fuzzy artificial neural networks. They are designed in two phases. In this case, the improvement is based on alpha cuts of fuzzy weight in the network connections. In the first phase, the optimal values of alpha cuts zero and one are obtained to define the place of a fuzzy weight for a network connection. Then, in the second phase, the optimal values of middle alpha cuts are obtained to define the shape of a fuzzy weight. The experiments for the two improved networks are performed in terms of generated error and execution time. The results tested were based on available rule/data sets in University of California Irvine (UCI) machine learning repository. Data sets were used for GA-based fuzzy granular neural networks, and rule sets were used for GA-based fuzzy artificial neural networks. The rule sets used were customer satisfaction, uranium, and the datasets used were wine, iris, servo, concrete compressive strength, and uranium. The results for the two-phase networks revealed the improvements of these methods over the conventional onephase networks. The two-phase GA-based fuzzy artificial neural networks improved 35% and 98% for execution time, and 27% and 26% for the generated error. The results for GA-based granular neural networks were revealed in comparison with GA-based crisp artificial neural networks. The comparison with other related granular computing methods were done using the iris benchmark data set. The results for these networks showed an average performance of 82.1%. The results from the proposed methods were analyzed in terms of statistical measurements for rule strengths and classifier performance using benchmark medical datasets. Therefore, this thesis has shown GA-based fuzzy granular neural networks, and GA-based fuzzy artificial neural networks are capable of reasoning based on granules for both crisp and uncertain data in artificial neural networks

Express Prediction Of External Distinctive Features Of Person Using The Program Of Dermatoglyphics For Prediction

The aim of our study was to investigate the current state of computer identification applications, such as artificial neural networks. The material of our study were antroposcopic and anthropometric parameters obtained from 180 male and females aged 18–55 years living in the Ivano-Frankivsk region and belonging to Boiko, Lemko or Hutsul ethno-territorial group. Prints of comb pattern of the toes obtained by scanning with Futronic\u27s FS80 USB2.0 Fingerprint Scanner using the program ftrScanApiEx.exe. followed by the transfer of data to a personal computer. For statistical processing of the obtained data we use STATISTICA 12 from the company StatSoft. Construction of neural networks was carried out using Neural Networks. As a result of our research there was carried out the prediction of anthropometric and antroposcopic parameters (ethno-territorial and gender belonging, etc.) through the use of dermatoglyphic parameters of the hands and feet in 180 people living in the Ivano-Frankivsk region. The proposed method allowed to obtain the results with a forecasts probability 73–90 %. The use of above algorithm of actions allowed a 50 % increase of quality of identification of unknown person for using dermatoglyphic method and 67 % facilitatation of the process of identification (of quantitative and qualitative calculations, determining correlations between parameters) in comparison with previously known manner. Therefore, our proposed method can be used as an express diagnostics of common phenotypic traits of the person (ethno-territorial affiliation, gender, etc.) at admission of mass victims (natural disasters, acts of terrorism, armed conflicts, man-made disasters, etc.), it doesn\u27t not require a long time for conducting, specially trained staff and is inexpensive.Conclusions: The possibility of predicting external-recognizing features of a person such as etno-racial belonging, sex, anthropometric and antroposcopic parameters will allow widely use dermatoglyphic method at the level with other methods in conducting forensic identification of impersonal, fragmented and putrefactive modified corpses

Fault Detection in Ball Bearings

Ball bearing joints are a critical component in all rotating machinery, and detecting and locating faults in these joints is a significant problem in industry and research. Intelligent fault detection (IFD) is the process of applying machine learning and other statistical methods to monitor the health states of machines. This paper explores the construction of vibration images, a preprocessing technique that has been previously used to train convolutional neural networks for ball bearing joint IFD. The main results demonstrate the robustness of this technique by applying it to a larger dataset than previously used and exploring the hyperparameters used in constructing the vibration images

Artificial Neural Network in Cosmic Landscape

In this paper we propose that artificial neural network, the basis of machine learning, is useful to generate the inflationary landscape from a cosmological point of view. Traditional numerical simulations of a global cosmic landscape typically need an exponential complexity when the number of fields is large. However, a basic application of artificial neural network could solve the problem based on the universal approximation theorem of the multilayer perceptron. A toy model in inflation with multiple light fields is investigated numerically as an example of such an application.Comment: v2, add some new content