1,028 research outputs found
Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model
The aim of this study is to show how a Kohonen map can be used to increase the forecasting horizon of a financial failure model. Indeed, most prediction models fail to forecast accurately the occurrence of failure beyond one year, and their accuracy tends to fall as the prediction horizon recedes. So we propose a new way of using a Kohonen map to improve model reliability. Our results demonstrate that the generalization error achieved with a Kohonen map remains stable over the period studied, unlike that of other methods, such as discriminant analysis, logistic regression, neural networks and survival analysis, traditionally used for this kind of task
SOM-based algorithms for qualitative variables
It is well known that the SOM algorithm achieves a clustering of data which
can be interpreted as an extension of Principal Component Analysis, because of
its topology-preserving property. But the SOM algorithm can only process
real-valued data. In previous papers, we have proposed several methods based on
the SOM algorithm to analyze categorical data, which is the case in survey
data. In this paper, we present these methods in a unified manner. The first
one (Kohonen Multiple Correspondence Analysis, KMCA) deals only with the
modalities, while the two others (Kohonen Multiple Correspondence Analysis with
individuals, KMCA\_ind, Kohonen algorithm on DISJonctive table, KDISJ) can take
into account the individuals, and the modalities simultaneously.Comment: Special Issue apr\`{e}s WSOM 03 \`{a} Kitakiush
How to improve robustness in Kohonen maps and display additional information in Factorial Analysis: application to text mining
This article is an extended version of a paper presented in the WSOM'2012
conference [1]. We display a combination of factorial projections, SOM
algorithm and graph techniques applied to a text mining problem. The corpus
contains 8 medieval manuscripts which were used to teach arithmetic techniques
to merchants. Among the techniques for Data Analysis, those used for
Lexicometry (such as Factorial Analysis) highlight the discrepancies between
manuscripts. The reason for this is that they focus on the deviation from the
independence between words and manuscripts. Still, we also want to discover and
characterize the common vocabulary among the whole corpus. Using the properties
of stochastic Kohonen maps, which define neighborhood between inputs in a
non-deterministic way, we highlight the words which seem to play a special role
in the vocabulary. We call them fickle and use them to improve both Kohonen map
robustness and significance of FCA visualization. Finally we use graph
algorithmic to exploit this fickleness for classification of words
Fault prediction in aircraft engines using Self-Organizing Maps
Aircraft engines are designed to be used during several tens of years. Their
maintenance is a challenging and costly task, for obvious security reasons. The
goal is to ensure a proper operation of the engines, in all conditions, with a
zero probability of failure, while taking into account aging. The fact that the
same engine is sometimes used on several aircrafts has to be taken into account
too. The maintenance can be improved if an efficient procedure for the
prediction of failures is implemented. The primary source of information on the
health of the engines comes from measurement during flights. Several variables
such as the core speed, the oil pressure and quantity, the fan speed, etc. are
measured, together with environmental variables such as the outside
temperature, altitude, aircraft speed, etc. In this paper, we describe the
design of a procedure aiming at visualizing successive data measured on
aircraft engines. The data are multi-dimensional measurements on the engines,
which are projected on a self-organizing map in order to allow us to follow the
trajectories of these data over time. The trajectories consist in a succession
of points on the map, each of them corresponding to the two-dimensional
projection of the multi-dimensional vector of engine measurements. Analyzing
the trajectories aims at visualizing any deviation from a normal behavior,
making it possible to anticipate an operation failure.Comment: Communication pr\'esent\'ee au 7th International Workshop WSOM 09, St
Augustine, Floride, USA, June 200
The self organizing map of neighbour stars and its kinematical interpretation
The Self-Organizing Map (SOM) is a neural network algorithm that has the special property ofcreating spatially organized tepresetüatioes of various features of input signals. The resulting maps resemble realneural structures found in the cortices of developed animal brains.: Also, the SOM. has been successful in various pattern recognition tasks involving noisy signals, as for instance, speech recognition and for this reason we are studying its application to some astronomical problems. In this paper w~ present the 2-D mapping and subsequerÃt study of one local sample of 12000 stars using SOM. The available attributes are 14: 3-D position and velocitiesvphotometric indexes, spectral type and luminosity class. The possible location of halo, thick disk and thin disk stars is discussed. Their kinematical properties are also compared using the velocity distribution moments up to order four.Peer ReviewedPostprint (published version
Multidimensional Urban Segregation - Toward A Neural Network Measure
We introduce a multidimensional, neural-network approach to reveal and
measure urban segregation phenomena, based on the Self-Organizing Map algorithm
(SOM). The multidimensionality of SOM allows one to apprehend a large number of
variables simultaneously, defined on census or other types of statistical
blocks, and to perform clustering along them. Levels of segregation are then
measured through correlations between distances on the neural network and
distances on the actual geographical map. Further, the stochasticity of SOM
enables one to quantify levels of heterogeneity across census blocks. We
illustrate this new method on data available for the city of Paris.Comment: NCAA S.I. WSOM+ 201
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