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For many pattern recognition problems, the relation between the sample vectors and the class labels are known during the data acquisition procedure. However, how to find the useful rules or knowledge hidden in the data is very important and challengeable. Rule extraction methods are very useful in mining the important and heuristic knowledge hidden in the original high-dimensional data. It can help us to construct predictive models with few attributes of the data so as to provide valuable model interpretability and less training times.

In this paper, a novel rule extraction method with the application of biclustering algorithm is proposed.

To choose the most significant biclusters from the huge number of detected biclusters, a specially modified information entropy calculation method is also provided. It will be shown that all of the important knowledge is in practice hidden in these biclusters.

The novelty of the new method lies in the detected biclusters can be conveniently translated into if-then rules. It provides an intuitively explainable and comprehensive approach to extract rules from high-dimensional data while keeping high classification accuracy.

The term “agent-based modelling” (ABM) is a buzzword which is widely used in the scientific literature even though it refers to a variety of methodologies implemented in different disciplinary contexts. The numerous works dealing with ABM require a clarification to better understand the lines of thinking paved by this approach in economics. All modelling tasks are a means and a source of knowledge, and this epistemic function can vary depending on the methodology. this paper is to present four major ways (deductive, abductive, metaphorical and phenomenological) of implementing an agent-based framework to describe economic systems. ABM generates numerous debates in economics and opens the room for epistemological questions about the micro-foundations of macroeconomics; before dealing with this issue, the purpose of this paper is to identify the kind of ABM the author can find in economics.

The profusion of works dealing with ABM requires a clarification to understand better the lines of thinking paved by this approach in economics. This paper offers a conceptual classification outlining the major trends of ABM in economics.

There are four categories of ABM in economics.

This paper suggests a methodological categorization of ABM works in economics.

The purpose of this guest editorial is to present an overview of the contributions in this special issue and proposes a positive approach to heuristics deriving from the growing interest in the decision-making topic with respect to the new challenges emerging in uncertain environments in management and marketing research.

The authors explore the reasons for a positive view of business actors' judgments and choices based on heuristics, not only in terms of effectiveness in practice, but their fit with human cognition and behavior, and the potential distinctiveness in contexts where technological devices and algorithms are more widespread, but not necessarily more appropriate.

The authors present and discuss the emergence and evolution of heuristics as a topic in the management literature, and the themes and insights proposed in the papers published in this special issue contributing to research aimed at systemizing a managerial perspective of the concepts and tools that may be useful for practitioners and researchers in this field.

The paper discusses the positive role that heuristics can play, offering some propositions for future research by framing heuristics as a set of tools (toolbox) for business actors in uncertain contexts, without constituting a cognitive limitation for effective solutions.

This work presents a system based on an ensemble of Convolutional Neural Networks (CNNs) and descriptors for bioimage classification that has been validated on different datasets of color images. The proposed system represents a very simple yet effective way of boosting the performance of trained CNNs by composing multiple CNNs into an ensemble and combining scores by sum rule. Several types of ensembles are considered, with different CNN topologies along with different learning parameter sets. The proposed system not only exhibits strong discriminative power but also generalizes well over multiple datasets thanks to the combination of multiple descriptors based on different feature types, both learned and handcrafted. Separate classifiers are trained for each descriptor, and the entire set of classifiers is combined by sum rule. Results show that the proposed system obtains state-of-the-art performance across four different bioimage and medical datasets. The MATLAB code of the descriptors will be available at https://github.com/LorisNanni.

A real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.

The architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.

The authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.

This paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.

This study aims to analyse the observation that the economics of many countries have boomed after the input of substantial investments into physical and social infrastructures.

A structural equation model is formulated to examine the effect of transportation infrastructure on the relationship between institutions and gross domestic product per capita (GDPPC). This study further differentiates between developed and developing economies.

The study identifies the different roles of transportation infrastructure in mediating the relationship between institutions and average income in these two types of economy. Institutions and transportation infrastructure positively influence GDPPC, whereas institutions positively influence transportation infrastructure. In addition, the results found indirect influence of institutions on GDPPC via transportation infrastructure.

This study provides new insights into international business studies based on institutional theory and factor-mobility theory.

This study examines the role of behavioural factors, such as confidence, optimism, pessimism and rational expectation, in affecting investment decisions in the Pakistani stock market.

Using daily trading data of Karachi Stock Exchange-100 index from January 2012 to December 2015, different regression models, including descriptive statistics and stationarity tests, are performed.

Results indicate that stock market trading has suffered from pessimistic behaviour of investors. In the first model, the authors find a positive sign of confidence and negative sign of optimism with the trading volume. The second model shows a positive role of confidence and rational expectations in affecting the trading volume in daily, Monday and Friday samples. The results of the third model show a negative sign of both optimism and rational expectation with the trading volume. Furthermore, the next model shows a negative sign of confidence combined with pessimism while testing their relationship with the trading volume. Finally, results of the final model suggest that optimism negatively affects the trading volume, and on the other hand, pessimism has a positive impact on the trading volume.

The method and empirical testing of behavioural biases and their relationship with economic variable used in this study seem to be a promising way to better understand the role of psychology in deriving financial decisions for academics and policymakers.

This study uses secondary data for measuring behavioural biases and decomposes the effect between rational expectation and behavioural biases.

Many organizations struggle to achieve their desired levels of business process flexibility and support. However, these two capabilities conflict with each other and different tradeoffs have to be made. In this paper, the authors analyze different process conceptualizations and discuss their implications. The authors argue that the conceptualizations people adopt to think (conceptualize) about business processes affect the way they model them, which in turn result in different flexibility-support tradeoffs.

A set of properties is proposed to compare process conceptualizations: dominant concept, contract, and existential and representational properties. Using these properties, several process conceptualizations are analyzed and integrated in a comparison chart, which highlights different flexibility-support tradeoffs. The storytelling method is adopted to support the analytic process.

The authors show how different process conceptualizations result in different flexibility-support tradeoffs. The authors suggest that we need to intervene on a set of properties of process conceptualizations to achieve different flexibility-support tradeoffs.

This research contributes to understanding the relationships between process conceptualizations, process modeling, and the flexibility-support tradeoff. A comparison chart helps organizations analyze their desired levels of flexibility and support using a set of properties.

The extent of covered viewpoints makes this study unique in the process management field. Such effort provides a contribution towards a more multidisciplinary discussion of process models, which integrates different process conceptualizations.