A comprehensible SOM-based scoring system.

The significant growth of consumer credit has resulted in a wide range of statistical and non-statistical methods for classifying applicants in 'good' and 'bad' risk categories. Traditionally, (logistic) regression used to be one of the most popular methods for this task, but recently some newer techniques like neural networks and support vector machines have shown excellent classification performance. Self-organizing maps (SOMs) have existed for decades and although they have been used in various application areas, only little research has been done to investigate their appropriateness for credit scoring. In this paper, it is shown how a trained SOM can be used for classification and how the basic SOM-algorithm can be integrated with supervised techniques like the multi-layered perceptron. Classification accuracy of the models is benchmarked with results reported previously.Decision; Knowledge; Knowledge discovery; Systems; Growth; Credit; Methods; Risk; Regression; Neural networks; Networks; Classification; Performance; Area; Research; Credit scoring; Models; Model;

ASPECT: A spectra clustering tool for exploration of large spectral surveys

We present the novel, semi-automated clustering tool ASPECT for analysing voluminous archives of spectra. The heart of the program is a neural network in form of Kohonen's self-organizing map. The resulting map is designed as an icon map suitable for the inspection by eye. The visual analysis is supported by the option to blend in individual object properties such as redshift, apparent magnitude, or signal-to-noise ratio. In addition, the package provides several tools for the selection of special spectral types, e.g. local difference maps which reflect the deviations of all spectra from one given input spectrum (real or artificial). ASPECT is able to produce a two-dimensional topological map of a huge number of spectra. The software package enables the user to browse and navigate through a huge data pool and helps him to gain an insight into underlying relationships between the spectra and other physical properties and to get the big picture of the entire data set. We demonstrate the capability of ASPECT by clustering the entire data pool of 0.6 million spectra from the Data Release 4 of the Sloan Digital Sky Survey (SDSS). To illustrate the results regarding quality and completeness we track objects from existing catalogues of quasars and carbon stars, respectively, and connect the SDSS spectra with morphological information from the GalaxyZoo project.Comment: 15 pages, 14 figures; accepted for publication in Astronomy and Astrophysic

Self-Organizing Maps. An application to the OGLE data and the Gaia Science Alerts

Self-Organizing Map (SOM) is a promising tool for exploring large multi-dimensional data sets. It is quick and convenient to train in an unsupervised fashion and, as an outcome, it produces natural clusters of data patterns. An example of application of SOM to the new OGLE-III data set is presented along with some preliminary results. Once tested on OGLE data, the SOM technique will also be implemented within the Gaia mission's photometry and spectrometry analysis, in particular, in so-called classification-based Science Alerts. SOM will be used as a basis of this system as the changes in brightness and spectral behaviour of a star can be easily and quickly traced on a map trained in advance with simulated and/or real data from other surveys.Comment: Presented as a poster at the "Classification and Discovery in Large Astronomical Surveys" meeting, Ringberg Castle, 14-17 October, 200

One-Shot Multi-Winner Self-Organizing Maps

There exist two different approaches to self-organizing maps (SOMs). One approach, rooted in theoretical neuroscience, uses SOMs as computational models of biological cortex. The other approach, taken in computer science and engineering, views SOMs as tools suitable to perform, for example, data visualization and pattern classification tasks. While the first approach emphasizes fidelity to neurobiological data, the latter stresses computational efficiency and effectiveness. In the research reported here, I developed and studied a class of SOMs that incorporates the multiple, simultaneous winner nodes implicit in many biologically-oriented SOMs, but determines the winners using the same efficient one-shot algorithm employed by computationally-oriented, single-winner SOMs. This was achieved by generalizing single-winner SOMs, using localized competitions. The resulting one-shot multi-winner SOM was found to support the formation of multiple adjacent, mirror-symmetric topographic maps. It constitutes the first computational model of mirror-image map formation, and raises questions about the role of Hebbian-type synaptic changes in the formation of mirror-symmetric maps that are often observed in the sensory neocortex of many species, including humans. The model unexpectedly predicted the occasional occurrence of adjacent, rotationally symmetric maps. It is natural to speculate that such atypically oriented maps might contribute to abnormal cortical information processing in some neurodevelopmental disorders. Traditional SOMs lack applicability to problems where the inputs are not single patterns, but temporal sequences of patterns. Several SOM extensions have been proposed as a remedy, but there is no standard for processing temporal sequences with SOMs. I focused on the task of learning unique spatial representations for non-trivial sets of temporal sequences. The one-shot multi-winner SOM extended by temporally-asymmetric Hebbian synapses proved effective when applied to this task. The learned representations retained information about sequence similarity. The feature maps that formed show that temporal sequence processing and map formation are not mutually exclusive. Since the sequence processing one-shot multi-winner SOM was trained with phonetic transcriptions of spoken words, the results can be related to the internalization of spoken words during language acquisition. A final redesign of the network and the subsequent multi-objective optimization of its parameters using a genetic algorithm produced a more effective system

Radio Galaxy Zoo: Knowledge Transfer Using Rotationally Invariant Self-Organising Maps

With the advent of large scale surveys the manual analysis and classification of individual radio source morphologies is rendered impossible as existing approaches do not scale. The analysis of complex morphological features in the spatial domain is a particularly important task. Here we discuss the challenges of transferring crowdsourced labels obtained from the Radio Galaxy Zoo project and introduce a proper transfer mechanism via quantile random forest regression. By using parallelized rotation and flipping invariant Kohonen-maps, image cubes of Radio Galaxy Zoo selected galaxies formed from the FIRST radio continuum and WISE infrared all sky surveys are first projected down to a two-dimensional embedding in an unsupervised way. This embedding can be seen as a discretised space of shapes with the coordinates reflecting morphological features as expressed by the automatically derived prototypes. We find that these prototypes have reconstructed physically meaningful processes across two channel images at radio and infrared wavelengths in an unsupervised manner. In the second step, images are compared with those prototypes to create a heat-map, which is the morphological fingerprint of each object and the basis for transferring the user generated labels. These heat-maps have reduced the feature space by a factor of 248 and are able to be used as the basis for subsequent ML methods. Using an ensemble of decision trees we achieve upwards of 85.7% and 80.7% accuracy when predicting the number of components and peaks in an image, respectively, using these heat-maps. We also question the currently used discrete classification schema and introduce a continuous scale that better reflects the uncertainty in transition between two classes, caused by sensitivity and resolution limits