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Abstract: In this study we develop an automatic detection model for discovering erroneous tax reports. The model uses a variety of neural network applications inclusive of the Multi-Layer Perceptrons (MLPs), Learning Vector Quantization (LVQ), decision tree, and Hyper-Rectangular Composite Neural Network (HRCNN) methods. Detailed taxation information from construction companies registered in the northern Taiwan region is sampled, giving a total of 5769 tax reports from 3172 construction companies which make up 35.98% of the top-three-class construction companies. The results confirm that the model yields a better recognition rate for distinguishing erroneous tax reports from the others. The automatic model is thus proven feasible for detecting erroneous tax reports. In addition, we note that the HRCNN yields a correction rate of 78% and, furthermore, generates 248 valuable rules, providing construction practitioners with criteria for preventing the submission of erroneous tax reports. [Copyright &y& Elsevier]

Abstract: To improve the accurate rate of mapping multi-spectral remote sensing images, in this paper we construct a class of HyperRectangular Composite Neural Networks (HRCNNs), integrating the paradigms of neural networks with the rule-based approach. The supervised decision-directed learning (SDDL) algorithm is also adopted to construct a two-layer network in a sequential manner by adding hidden nodes as needed. Thus, the classification knowledge embedded in the numerical weights of trained HRCNNs can be extracted and represented in the form of If-Then rules. The rules facilitate justification on the responses to increase accuracy of the classification. A sample of remote sensing image containing forest land, river, dam area, and built-up land is used to examine the proposed approach. The accurate recognition rate reaching over 99% demonstrates that the proposed approach is capable of dealing with image mapping. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach to constructing a neural tree to integrate the advantages of decision trees and neural networks. The proposed neural tree, called a quadratic-neuron-based neural tree (QUANT), is a tree-structured neural network composed of neurons with quadratic neural-type junctions for pattern classification. A quadratic neuron is capable of forming a hyper-ellipsoid that can be varied in sizes and in locations on the space spanned by the input variables. Via a batch-mode training algorithm, the QUANT grows a neural tree containing quadratic neurons in its nodes. These quadratic neurons recursively partition the feature space into hyper-ellipsoidal-shaped sub-regions. The QUANT has the partial incremental capability so that it does not need to re-construct a new neural tree to accommodate new training data whenever new data are introduced to a trained QUANT. To demonstrate the performance of the proposed QUANT, one pattern recognition problem and the spam e-mail detection problem were tested. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a new data projection algorithm which was inspired by the foraging behaviors of doves is proposed. We name the new data projection the swarm-inspired projection (SIP) algorithm. The algorithm allows us to visually estimate the number of clusters existing in a data set. Based on the projection result, we may then partition the data set into the corresponding number of clusters. The SIP algorithm regards each data pattern in a data set as a piece of crumb which will be sequentially tossed to a flock of doves on the ground. The doves will adjust their physical positions to compete for crumbs. Gradually, the flock of doves will be divided into several groups according to the distributions of the crumbs. The formed groups will naturally correspond to the underlying data structures in the data set. By viewing the scatter plot of the final positions of the doves we can estimate the number of clusters existing in the data set. Several data sets were used to demonstrate the effectiveness of the proposed SIP algorithm. [Copyright &y& Elsevier]

Abstract: Many man-made objects are composed of a number of some simple geometric shapes such as lines, circles, rectangles, etc. Therefore, the detection of rectangular objects is an important issue to some practical applications such as the detection of buildings and vehicles in aerial imagery, the detection of license plates in car images, etc. Several methods have been proposed for solving the problem of the detection of rectangular objects. While some approaches are based on the detecting lines, some approaches are based on the Hough transform. Each approach has its own advantages and disadvantages (e.g., computational load). In this paper, we propose a class of neural networks with a special type of neural junctions for the detection of rectangular objects. The proposed neural networks can be trained in either an unsupervised mode or a batch mode. In contrast to some popular clustering algorithms such as the fuzzy -means algorithm and the fuzzy -rectangular shells algorithm, our approach is not based on minimizing an objective function but based on the idea of competitive learning. Based on the idea of competitive learning, the computational load can be decreased. Several data sets were tested to illustrate the effectiveness of our proposed approach. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach to incrementally constructing a neural network that is capable of learning new information without forgetting old knowledge. The proposed neural network, called hyper-spherical ARTMAP network (HS-ARTMAP network), is a synthesis of an RBF-network-like module and an ART-like module. The HS-ARTMAP network is trained via a training algorithm similar to the training algorithm for the fuzzy ARTMAP system. To demonstrate the performance of the proposed HS-ARTMAP network, several pattern recognition and function approximation problems were tested. [Copyright &y& Elsevier]

Abstract: A classifier system is a machine learning system that learns syntactically simple string rules (called classifiers) through a genetic algorithm to guide its performance in an arbitrary environment. In a classifier system, the bucket brigade algorithm is used to solve the problem of credit assignment, which is a critical problem in the field of reinforcement learning. In this paper, we propose a new approach to fuzzy classifier systems and a neuro-fuzzy system referred to as ACSNFIS to implement the proposed fuzzy classifier system. The proposed system is tested by the balancing problem of a cart pole and the back-driving problem of a truck to demonstrate its performance. [Copyright &y& Elsevier]