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Data-driven housing-market segmentation has been given increasing prominence for its objectiveness in identifying submarkets based on the housing data's underlying structures. However, when handling high-dimensionality housing dataset, traditional statistical-clustering methods have been found to tend to lose low-variance information of the dataset and be deficient in deriving the globally optimal number of submarkets. Accordingly, with the intention of achieving more rigorous high-dimensionality housing market segmentation, a swarm-inspired projection (SIP) algorithm is introduced by this study. Using a high-dimensionality Taipei city's housing dataset in a case study, a comparison of the proposed SIP algorithm and a statistical-clustering method using the combination of principal component analysis (PCA) and K-means clustering is conducted in evaluating the predictive accuracy of hedonic price models of the housing submarkets. The results show that, as compared to the original single market, the segmented submarkets resulting from SIP algorithm are more homogenous and distinctive, where the resulted hedonic price models have high-level statistical explanation and disparate sets of hedonic prices for different submarkets. In addition, as compared to the use of a statistical-clustering method, SIP algorithm is found to obtain a more optimal number of submarkets, where the resulted hedonic price models are found to achieve greater improvement of statistical explanation and more stable reduction of prediction error. These findings highlight the advantages of our proposed SIP algorithm in high-dimensionality housing market segmentation, and thus it is hoped that the present research will serve as a practical tool to better inform further studies aimed at market-segmentation-related problems. [ABSTRACT FROM AUTHOR]

The authors developed a self-organizing map-based optimization (SOMO) neurofuzzy classifier to deal with a practical expatriation willingness (EW) problem, which is associated with employees’ willingness to accept expatriate assignments. The proposed model also delivers more information about rule coverage and generates user-friendly outcomes. The authors adopt the SOMO algorithm to optimize the weights of neurons in the proposed hybrid neurofuzzy classifier. They evaluated the model’s feasibility using the databases from previous studies exploring employees’ EW. The results show that the proposed neurofuzzy classifier yields 11 determination rules whose accuracy rates are greater than 80 percent. The contribution to the body of knowledge lies in the significant improvement in accuracy rates and coverage for predicting EW rules, optimization for all the network parameters using a novel algorithm, and more friendly outputs for practical use. [ABSTRACT FROM PUBLISHER]

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]