Evaluation of sampling methods for learning from imbalanced data

The problem of learning from imbalanced data is of critical importance in a large number of application domains and can be a bottleneck in the performance of various conventional learning methods that assume the data distribution to be balanced. The class imbalance problem corresponds to dealing with the situation where one class massively outnumbers the other. The imbalance between majority and minority would lead machine learning to be biased and produce unreliable outcomes if the imbalanced data is used directly. There has been increasing interest in this research area and a number of algorithms have been developed. However, independent evaluation of the algorithms is limited. This paper aims at evaluating the performance of five representative data sampling methods namely SMOTE, ADASYN, BorderlineSMOTE, SMOTETomek and RUSBoost that deal with class imbalance problems. A comparative study is conducted and the performance of each method is critically analysed in terms of assessment metrics. © 2013 Springer-Verlag.

Developing management capability within a horizontal supply chain in performance measurement deployment and evolution: A Dynamic Capabilities and Goal Theory perspective

Complex collaboration in rapidly changing business environments create challenges for management capability in Utility Horizontal Supply Chains (UHSCs) involving the deploying and evolving of performance measures. The aim of the study is twofold. First, there is a need to explore how management capability can be developed and used to deploy and evolve Performance Measurement (PM), both across a UHSC and within its constituent organisations, drawing upon a theoretical nexus of Dynamic Capability (DC) theory and complementary Goal Theory. Second, to make a contribution to knowledge by empirically building theory using these constructs to show the management motivations and behaviours within PM-based DCs. The methodology uses an interpretive theory building, multiple case based approach (n=3) as part of a USHC. The data collection methods include, interviews (n=54), focus groups (n=10), document analysis and participant observation (reflective learning logs) over a five-year period giving longitudinal data. The empirical findings lead to the development of a conceptual framework showing that management capabilities in driving PM deployment and evolution can be represented as multilevel renewal and incremental Dynamic Capabilities, which can be further understood in terms of motivation and behaviour by Goal-Theoretic constructs. In addition three interrelated cross cutting themes of management capabilities in consensus building, goal setting and resource change were identified. These management capabilities require carefully planned development and nurturing within the UHSC. 

Generating Data Augmented Spectroscopic Data For Performance Enhancement

The application of chemometrics in food science has revolutionized the field by allowing the creation of models able to automate a broad range of applications such as food authenticity and food fraud detection. In order to create effective and general models able to address the complexity of real life problems, a vast amount of varied training samples are required. Training dataset has to cover all possible types of sample and instrument variability. However, acquiring a varied amount of samples is a time consuming and costly process, in which collecting samples representative of the real world variation is not always possible, specially in some application fields. To address this problem, a novel framework for the application of data augmentation techniques to spectroscopic data has been designed and implemented. This is a carefully designed pipeline of four complementary and independent blocks which can be finely tuned depending on the desired variance for enhancing model's robustness: a) blending spectra, b) changing baseline, c) shifting along x axis, and d) adding random noise.
This novel data augmentation solution has been tested in order to obtain highly efficient generalised classification model based on spectroscopic data. Fourier transform mid-infrared (FT-IR) spectroscopic data of eleven pure vegetable oils (106 admixtures) for the rapid identification of vegetable oil species in mixtures of oils have been used as a case study to demonstrate the influence of this pioneering approach in chemometrics, obtaining a 10% improvement in classification which is crucial in some applications of food adulteration.

DACA: architecture to implement dynamic access control mechanisms on business tier components

Access control is a software engineering challenge in database applications. Currently, there is no satisfactory solution to dynamically implement evolving fine-grained access control mechanisms (FGACM) on business tiers of relational database applications. To tackle this access control gap, we propose an architecture, herein referred to as Dynamic Access Control Architecture (DACA). DACA allows FGACM to be dynamically built and updated at runtime in accordance with the established fine-grained access control policies (FGACP). DACA explores and makes use of Call Level Interfaces (CLI) features to implement FGACM on business tiers. Among the features, we emphasize their performance and their multiple access modes to data residing on relational databases. The different access modes of CLI are wrapped by typed objects driven by FGACM, which are built and updated at runtime. Programmers prescind of traditional access modes of CLI and start using the ones dynamically implemented and updated. DACA comprises three main components: Policy Server (repository of metadata for FGACM), Dynamic Access Control Component (DACC) (business tier component responsible for implementing FGACM) and Policy Manager (broker between DACC and Policy Server). Unlike current approaches, DACA is not dependent on any particular access control model or on any access control policy, this way promoting its applicability to a wide range of different situations. In order to validate DACA, a solution based on Java, Java Database Connectivity (JDBC) and SQL Server was devised and implemented. Two evaluations were carried out. The first one evaluates DACA capability to implement and update FGACM dynamically, at runtime, and, the second one assesses DACA performance against a standard use of JDBC without any FGACM. The collected results show that DACA is an effective approach for implementing evolving FGACM on business tiers based on Call Level Interfaces, in this case JDBC.

The economic consequences of Portuguese State’s movement towards business sector

Over the last 36 years, the relationship with the Portuguese state-owned enterprises registered several dynamics: nationalizations, privatizations and corporatization of public services. However, until now the State Business Sector from a national accounts perspective was never analyzed. Based on data collected and compiled for the first time at Statistics Portugal, this PhD thesis aims to test, analyzing in eight dimensions, whether the weight of the State Business Sector increased and if it contributed positively to the Portuguese economy, from 2006 to 2010. In addition to this analysis, an overview of the economic theory of state intervention in the economy, the paradigm changes of public policy in the international context, the evolution of the Portuguese State Business Sector since 1974, accompanied with a business and national accounting perspective between 2006 and 2010, are also presented. The results allow us to conclude that, in general, the weight of the State Business Sector in the Portuguese economy increased and had a tendency of a positive contribution to its economic growth. The State Business Sector also contributed positively to the nominal labour productivity (although with a decreasing trend of contribution to growth over the period under review) and the profitability of the non-financial corporations sector (although impairing the overall ratio of this sector). Nonetheless, the State Business Sector contributed negatively to the fairness in compensation of employees (although with an improvement trend) and to the competitiveness of labour cost, investment and sectorial sustainability of the Portuguese economy (reinforced by a falling trend). The results also suggest that the State Business Sector had an economic behaviour closer to a welfare maximizing model than to a profit maximizing model. This distinct performance with respect to the institutional sector in which is included, highlights the need to study and reassess the relationship of the state with public corporations, in light of agency theory using micro-data. Lastly, contributions to improve the economic performance of the State Business Sector and future prospects of evolution are presented.

Long-term performance of mergers and acquisitions in ASEAN countries

The study looks at mergers and acquisitions (M&As) in ASEAN countries and examines the post-M&A performance using data from 2001 to 2012. The industry-adjusted operating performance tends to decline in the 3 years following an M&A. Yet, the results suggest that M&As completed during the financial crisis are more profitable than those implemented before and/or after the crisis. We argue that this is mainly due to the synergies created between the firms’ resources during the crisis which augur well for firms’ economic performance. We find that, during the crisis, certain characteristics of the firms like the relative size of the target, cross-border nature of deals, acquirer's cash reserves and friendly nature of deals are important determinants of long-term post-M&A operating performance. However, for M&As during the crisis, there appears to be no relationship between performance and firms’ characteristics linked to M&A activity such as payment method, industry relatedness and percentage of target's share acquired.

Revisiting the relationship between marketing capabilities and firm performance: The moderating role of market orientation, marketing strategy and organisational power

This paper extends original insights of resource-advantage theory (Hunt & Morgan, 1995) to a specific analysis of the moderators of the capabilities-performance relationship such as market orientation, marketing strategy and organizational power. Using established measures and a representative sample of UK firms drawn from Verhoef and Leeflang’s data (2009), our study tests new hypotheses to explain how different types of marketing capabilities contribute to firm performance. The application of resource-advantage theory advances theorising on both marketing and organisational antecedents of firm performance and the causal mechanisms by which competitive advantage is generated.

Fractionating Human Intelligence

What makes one person more intellectually able than another? Can the entire distribution of human intelligence be accounted for by just one general factor? Is intelligence supported by a single neural system? Here, we provide a perspective on human intelligence that takes into account how general abilities or ‘‘factors’’ reflect the functional organiza- tion of the brain. By comparing factor models of individual differences in performance with factor models of brain functional organization, we demon- strate that different components of intelligence have their analogs in distinct brain networks. Using simulations based on neuroimaging data, we show that the higher-order factor ‘‘g’’ is accounted for by cognitive tasks corecruiting multiple networks. Finally, we confirm the independence of these com- ponents of intelligence by dissociating them using questionnaire variables. We propose that intelli- gence is an emergent property of anatomically distinct cognitive systems, each of which has its own capacity.

Learning business strategies for competitive electronic marketplaces

This paper presents a Multi-Agent Market simulator designed for developing new agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. This tool studies negotiations based on different market mechanisms and, time and behavior dependent strategies. The results of the negotiations between agents are analyzed by data mining algorithms in order to extract rules that give agents feedback to improve their strategies. The system also includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agent reactions.

A long-term risk management tool for electricity markets using swarm intelligence

This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.

Acqusition and data analysis of the eco-efficiency for the establishment of indicators parameters for industrial environmental sustainability

The industrial activity is inevitably associated with a certain degradation of the environmental quality, because is not possible to guarantee that a manufacturing process can be totally innocuous. The eco-efficiency concept is globally accepted as a philosophy of entreprise management, that encourages the companies to become more competitive, innovative and environmentally responsible by promoting the link between its companies objectives for excellence and its objectives of environmental excellence issues. This link imposes the creation of an organizational methodology where the performance of the company is concordant with the sustainable development. The main propose of this project is to apply the concept of eco-efficiency to the particular case of the metallurgical and metal workshop industries through the development of the particular indicators needed and to produce a manual of procedures for implementation of the accurate solution.

Consumers Performance Evaluation of the Participation in Demand Response Programs Using Baseline Methods

Demand response has gain increasing importance in the context of competitive electricity markets environment. The use of demand resources is also advantageous in the context of smart grid operation. In addition to the need of new business models for integrating demand response, adequate methods are necessary for an accurate determination of the consumers’ performance evaluation after the participation in a demand response event. The present paper makes a comparison between some of the existing baseline methods related to the consumers’ performance evaluation, comparing the results obtained with these methods and also with a method proposed by the authors of the paper. A case study demonstrates the application of the referred methods to real consumption data belonging to a consumer connected to a distribution network.

Developing and implementing an environmental performance index for the Portuguese military

Business Strategy and the Environment nº 15, p. 71–86

Information professionals in business context: new professionals, old skills

This communication aims to present some reflections regarding the importance of information in organizational context, especially in business context. The ability to produce and to share expertise and knowledge among its employees is now a key factor in the success of any organization. However, it’s also true that workers are increasingly feeling that too much information can hurt their performance. The existence of skilled professionals able to organize, evaluate, select and disseminate information in organizations appears to be a prerequisite for success. The skills necessary for the formation of a professional devoted to the management of information and knowledge in the context of business organizations will be analysed. Then data collected in two focus group discussion with students from a graduate course in Business Information, from Polytechnic Institute of Porto, Portugal, a will be examined.