Building Credit-Risk Evaluation Expert Systems Using Neural Network Rule Extraction and Decision Tables.

In this paper, we evaluate and contrast four neural network rule extraction approaches for credit scoring. Experiments are carried out on three real life credit scoring data sets. Both the continuous and the discretised versions of all data sets are analysed. The rule extraction algorithms, Neurolinear, Neurorule, Trepan and Nefclass, have different characteristics with respect to their perception of the neural network and their way of representing the generated rules or knowledge. It is shown that Neurolinear, Neurorule and Trepan are able to extract very concise rule sets or trees with a high predictive accuracy when compared to classical decision tree (rule) induction algorithms like C4.5(rules). Especially Neurorule extracted easy to understand and powerful propositional ifthen rules for all discretised data sets. Hence, the Neurorule algorithm may offer a viable alternative for rule generation and knowledge discovery in the domain of credit scoring.Credit; Information systems; International; Systems;

Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model

The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods). Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented

NORFREA: An algorithm for non redundant fuzzy rule extraction

This contribution presents a new algorithm (NORFREA) to select fuzzy rules from a grid partition of the input domain. Besides using an efficiency measure for the rules, this algorithm employs an heuristic technique to reduce the influence of the initial grid structure. Different benchmarks of classification problems are included to illustrate the advantages of this algorith

Класифікатор глибокого навчання на основі нейронної мережі NEFCLASS

It is proposed a new class of fuzzy classifiers. It is a deep learning classifier based onNEFCLASS neural network. The pre-learning is supplied with help of Restricted Boltzman MachineПредложен новый класс нечетких классификаторов. Это классификаторы глубокого обучения на основе нейронной сети NEFCLASS. Предварительное обучение обеспечивается с помощью ограниченной машины БольцманаЗапропоновано новий клас нечітких класифікаторів. Це класифікатори глибокого навчання на основі нейронної мережі NEFCLASS. Попереднє навчання забезпечується за допомогою обмеженої машини Больцман

Data-driven fuzzy rule generation and its application for student academic performance evaluation

Several approaches using fuzzy techniques have been proposed to provide a practical method for evaluating student academic performance. However, these approaches are largely based on expert opinions and are difficult to explore and utilize valuable information embedded in collected data. This paper proposes a new method for evaluating student academic performance based on data-driven fuzzy rule induction. A suitable fuzzy inference mechanism and associated Rule Induction Algorithm is given. The new method has been applied to perform Criterion-Referenced Evaluation (CRE) and comparisons are made with typical existing methods, revealing significant advantages of the present work. The new method has also been applied to perform Norm-Referenced Evaluation (NRE), demonstrating its potential as an extended method of evaluation that can produce new and informative scores based on information gathered from data

Information and Analytical Support for Management of Fire Extinguishing at Highly Dangerous and Technically Complex Facilities

In this article the authors formulate the task for process management of man-made fire extinguishing at highly dangerous and technically complex facilities. This task consists of fire localization and elimination using minimal assignment in minimum amount of time. For this task the authors developed a neuro-fuzzy model for fire extinguishing process control, the main elements of which are a neuro-fuzzy model for predicting the fire area, a neuro-fuzzy model for selecting the fire rank, a neuro-fuzzy model for evaluating the implementation success of the plan, a neuro-fuzzy model for selecting the optimal action plan, an analytical model for evaluation of resources sufficiency, an analytical model for resources selection, and a model for implementation of neuro-fuzzy models. In comparison with existing models, distinctive features of the developed model are the following: application of combined (bell-shaped with thresholds) membership functions that allow to performmore accurate approximation of input parameters values; implementation of the block to eliminate dynamic errors. This paper assesses model adequacy through verification and validation. The authors developed a system for fire extinguishing process control. This system allows us to raise of firefighters’ efficiency due to increase of accuracy of managerial decisions taken by the manager and time reduction needed to formulatea decision

Incremental Local Linear Fuzzy Classifier in Fisher Space

Optimizing the antecedent part of neurofuzzy system is an active research topic, for which different approaches have been developed. However, current approaches typically suffer from high computational complexity or lack of ability to extract knowledge from a given set of training data. In this paper, we introduce a novel incremental training algorithm for the class of neurofuzzy systems that are structured based on local linear classifiers. Linear discriminant analysis is utilized to transform the data into a space in which linear discriminancy of training samples is maximized. The neurofuzzy classifier is then built in the transformed space, starting from the simplest form (a global linear classifier). If the overall performance of the classifier was not satisfactory, it would be iteratively refined by incorporating additional local classifiers. In addition, rule consequent parameters are optimized using a local least square approach. Our refinement strategy is motivated by LOLIMOT, which is a greedy partition algorithm for structure training and has been successfully applied in a number of identification problems. The proposed classifier is compared to several benchmark classifiers on a number of well-known datasets. The results prove the efficacy of the proposed classifier in achieving high performance while incurring low computational effort

Three-dimensional hydrodynamic models coupled with GIS-based neuro-fuzzy classification for assessing environmental vulnerability of marine cage aquaculture

There is considerable opportunity to develop new modelling techniques within a Geographic Information Systems (GIS) framework for the development of sustainable marine cage culture. However, the spatial data sets are often uncertain and incomplete, therefore new spatial models employing “soft computing” methods such as fuzzy logic may be more suitable. The aim of this study is to develop a model using Neuro-fuzzy techniques in a 3D GIS (Arc View 3.2) to predict coastal environmental vulnerability for Atlantic salmon cage aquaculture. A 3D hydrodynamic model (3DMOHID) coupled to a particle-tracking model is applied to study the circulation patterns, dispersion processes and residence time in Mulroy Bay, Co. Donegal Ireland, an Irish fjard (shallow fjordic system), an area of restricted exchange, geometrically complicated with important aquaculture activities. The hydrodynamic model was calibrated and validated by comparison with sea surface and water flow measurements. The model provided spatial and temporal information on circulation, renewal time, helping to determine the influence of winds on circulation patterns and in particular the assessment of the hydrographic conditions with a strong influence on the management of fish cage culture. The particle-tracking model was used to study the transport and flushing processes. Instantaneous massive releases of particles from key boxes are modelled to analyse the ocean-fjord exchange characteristics and, by emulating discharge from finfish cages, to show the behaviour of waste in terms of water circulation and water exchange. In this study the results from the hydrodynamic model have been incorporated into GIS to provide an easy-to-use graphical user interface for 2D (maps), 3D and temporal visualization (animations), for interrogation of results. v Data on the physical environment and aquaculture suitability were derived from a 3- dimensional hydrodynamic model and GIS for incorporation into the final model framework and included mean and maximum current velocities, current flow quiescence time, water column stratification, sediment granulometry, particulate waste dispersion distance, oxygen depletion, water depth, coastal protection zones, and slope. The Neuro-fuzzy classification model NEFCLASS–J, was used to develop learning algorithms to create the structure (rule base) and the parameters (fuzzy sets) of a fuzzy classifier from a set of classified training data. A total of 42 training sites were sampled using stratified random sampling from the GIS raster data layers, and the vulnerability categories for each were manually classified into four categories based on the opinions of experts with field experience and specific knowledge of the environmental problems investigated. The final products, GIS/based Neuro Fuzzy maps were achieved by combining modeled and real environmental parameters relevant to marine fin fish Aquaculture. Environmental vulnerability models, based on Neuro-fuzzy techniques, showed sensitivity to the membership shapes of the fuzzy sets, the nature of the weightings applied to the model rules, and validation techniques used during the learning and validation process. The accuracy of the final classifier selected was R=85.71%, (estimated error value of ±16.5% from Cross Validation, N=10) with a Kappa coefficient of agreement of 81%. Unclassified cells in the whole spatial domain (of 1623 GIS cells) ranged from 0% to 24.18 %. A statistical comparison between vulnerability scores and a significant product of aquaculture waste (nitrogen concentrations in sediment under the salmon cages) showed that the final model gave a good correlation between predicted environmental vi vulnerability and sediment nitrogen levels, highlighting a number of areas with variable sensitivity to aquaculture. Further evaluation and analysis of the quality of the classification was achieved and the applicability of separability indexes was also studied. The inter-class separability estimations were performed on two different training data sets to assess the difficulty of the class separation problem under investigation. The Neuro-fuzzy classifier for a supervised and hard classification of coastal environmental vulnerability has demonstrated an ability to derive an accurate and reliable classification into areas of different levels of environmental vulnerability using a minimal number of training sets. The output will be an environmental spatial model for application in coastal areas intended to facilitate policy decision and to allow input into wider ranging spatial modelling projects, such as coastal zone management systems and effective environmental management of fish cage aquaculture

Application of Neuro-Fuzzy Technique+2:9s to Predict Ground Water Vulnerability in Northwest Arkansas

Contamination of ground water has been a major concern in recent years of local, state and federal agencies involved with the management, quality, and quantity of water and their relationships with human health. The Springfield Plateau aquifer, which lies beneath the study area in northwest Arkansas, has been shown to have higher nitrate-N (NO3-N) concentrations than the national median. The dominant landuse (LULC) of this area is agriculture (primarily pasture/cattle and woodlands) and an encroaching urbanization. The major sources of nitrogen in the study area are poultry/cattle wastes, inorganic fertilizers (Peterson et. al., 1998) and septic filter fields. Many of the soils in the Ozark Region are highly permeable and well drained and the geology is karst. The probability of pollution occurring at a given location is a function not only of its hydrogeologic setting but also of anthropogenic pollution in the area (Evans, 1990)