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The purpose of this paper is to analyze previous models of the concept of ranking accuracy within the weighted aggregated sum product assessment (WASPAS) method and make necessary refinements.

This paper presents a correct combination of the weighted sum model (WSM) and weighted product model (WPM), which is usually performed on an ad hoc basis in the literature.

One of the reasons of rarely conducting ranking accuracy analysis might be that some of the reported equations in the literature are confusing, and hence, accurate partial derivatives cannot be calculated. In this study, all of the necessary formulations are re-derived and necessary modifications are proposed.

A corrected WASPAS equation for optimal combination parameters is derived. Two examples are used to validate the formulations, and software implementation is provided. Because multiple attribute decision-making (MADM) has gained widespread attention from both the academia and industry, the findings of this paper help decision makers fully capitalize the concept of ranking accuracy and avoid possible confusions regarding the equations reported in the literature.

WASPAS is a relatively new MADM method and has enjoyed a visible position in the MADM literature. In addition to its simplicity, the WASPAS method utilizes the concept of ranking accuracy by combining the well-known WSM and WPM. This combination realized via an optimization criterion brings unique opportunities for decision makers such as evaluating confidence intervals for relative significance of alternatives and reducing estimated variance of ranking results. Despite its crucial importance, the combination of WSM and WPM is usually performed on an ad hoc basis in the literature. In this study, all of the necessary formulations are re-derived and necessary modifications are proposed along with clarifying examples.

The purpose of this paper is to investigate the applicability of meta‐learning to the problem of algorithm recommendation for gene expression data classification.

Meta‐learning was used to provide a preference order of machine learning algorithms, based on their expected performances. Two approaches were considered for such: k‐nearest neighbors and support vector machine‐based ranking methods. They were applied to a set of 49 publicly available microarray datasets. The evaluation of the methods followed standard procedures suggested in the meta‐learning literature.

Empirical evidences show that both ranking methods produce more interesting suggestions for gene expression data classification than the baseline method. Although the rankings are more accurate, a significant difference in the performances of the top classifiers was not observed.

As the experiments conducted in this paper suggest, the use of meta‐learning approaches can provide an efficient data driven way to select algorithms for gene expression data classification.

This paper reports contributions to the areas of meta‐learning and gene expression data analysis. Regarding the former, it supports the claim that meta‐learning can be suitably applied to problems of a specific domain, expanding its current practice. To the latter, it introduces a cost effective approach to better deal with classification tasks.

In today’s digital era, data pertaining to scientific research have attracted considerable attention of researchers. Data of scientific publications can be modeled in the form of networks such as citation networks, co-citation networks, collaboration networks, and others. Identification and ranking of important nodes in such networks is useful in many applications, such as finding most influential papers, most productive researchers, pattern of citation, and many more. The paper aims to discuss this issue.

A number of methods are available in literature for node ranking, and K-shell decomposition is one such method. This method categorizes nodes in different groups based on their topological position. The shell number of a node provides useful insights about the node’s importance in the network. It has been found that shells produced by the K-shell method need to be further refined to quantify the influence of the nodes aptly. In this work, a method has been developed, which ranks nodes by taking the core(s) as the origin and second-order neighborhood of a node as its immediate sphere of influence.

It is found that the performance of the proposed technique is either comparable or better than other methods in terms of correctness and accuracy. In case of assigning different ranks to nodes, the performance of the proposed technique is far more superior to existing methods. The proposed method can be used to rank authors, research articles, and fields of research.

The proposed method ranks nodes by their global position in a network as well as their local sphere of information. It leads to better quantification of a node’s impact. This method is found to be better in terms of accuracy and correctness. In case of assigning different ranks to nodes, the performance of the proposed technique is far more superior to existing methods.

This study explores the use and perceptions of scholarly journal ranking lists in the management field based on stakeholders’ lived experience.

The results are based on a survey of 463 active knowledge management and intellectual capital researchers.

Journal ranking lists have become an integral part of contemporary management academia: 33% and 37% of institutions and individual scholars employ journal ranking lists, respectively. The Australian Business Deans Council (ABDC) Journal Quality List and the UK Academic Journal Guide (AJG) by the Chartered Association of Business Schools (CABS) are the most frequently used national lists, and their influence has spread far beyond the national borders. Some institutions and individuals create their own journal rankings.

Management researchers employ journal ranking lists under two conditions: mandatory and voluntary. The forced mode of use is necessary to comply with institutional pressure that restrains the choice of target outlets. At the same time, researchers willingly consult ranking lists to advance their personal career, maximize their research exposure, learn about the relative standing of unfamiliar journals, and direct their students. Scholars, academic administrators, and policymakers should realize that journal ranking lists may serve as a useful tool when used appropriately, in particular when individuals themselves decide how and for what purpose to employ them to inform their research practices.

The findings reveal a journal ranking lists paradox: management researchers are aware of the limitations of ranking lists and their deleterious impact on scientific progress; however, they generally find journal ranking lists to be useful and employ them.

Studies the problem of forecasting demand on the article level in a competitive consumer goods market. First the conventional approach to forecasting is discussed. A number of weak points of the demand forecasting unit approach are identified. Next, a new approach to forecasting based on applying scaling models, is presented. The method is then tried out and evaluated in the context of a real life business case. Shows that the advantage of the assortment forecasting process is its simplicity and strong means for feedback. Combined with a strong focus on consumer values, the method has potential to produce reliable forecast based on promotion and assortment change inputs.

ARIMA models have been extensively examined in the context of the real estate market. The purpose of this paper is to examine issues relating to their application in a forecasting context. Specifically, the paper seeks to examine whether in‐sample measures of best‐fit and also past forecasting accuracy bear any relation to future forecasting performance.

The forecasting performance of alternative ARIMA specifications are compared over rolling estimation and forecasting windows. The forecasting accuracy of the alternative specifications is compared with specific attention placed on the accuracy of the respective specification that in‐sample provides the best fitting model.

The results highlight the limitations in using the conventional approach to identifying the best‐specified ARIMA model in sample, when the purpose of the analysis is to provide forecasts. The results show that while ARIMA models can be useful in anticipating broad market trends, there are substantial differences in the forecasts obtained using alternative specifications. The use of conventional measures of best‐fit provide little indication as to future forecasting ability, nor does the forecasting performance of a specification in previous periods.

ARIMA modelling has frequently been highlighted as a useful forecasting approach. This paper illustrates that care needs to be paid in their use in a forecasting context and full appreciation of the strengths and limitations of the ARIMA approach.

This study investigates whether the skilled minority of active equity managers in emerging markets can be identified using a machine learning (ML) framework that incorporates a large set of performance characteristics.

The study uses a cross-section of South African active equity managers from January 2002 to December 2021. The performance characteristics are analysed using ML models, with a particular focus on gradient boosters, and naïve selection techniques such as momentum and style alpha. The out-of-sample nominal, excess and risk-adjusted returns are evaluated, and precision tests are conducted to assess the accuracy of the performance predictions.

A minority of active managers exhibit skill that results in generating alpha, even after accounting for fees, and show that ML models, particularly gradient boosters, are superior at identifying non-linearities. LightGBM (LG) achieves the highest out-of-sample nominal, excess and risk-adjusted return and proves to be the most accurate predictor of performance in precision tests. Naïve selection techniques, such as momentum and style alpha, outperform most ML models in forecasting emerging market active manager performance.

The authors contribute to the literature by demonstrating that a ML approach that incorporates a large set of performance characteristics can be used to identify skilled active equity managers in emerging markets. The findings suggest that both ML models and naïve selection techniques can be used to predict performance, but the former is more accurate in predicting ex ante performance. This study has practical implications for investment practitioners and academics interested in active asset manager performance in emerging markets.

A necessary preprocessing of document classification is to label some documents so that a classifier can be built based on which the remaining documents can be classified. Because each document differs in length and complexity, the cost of labeling each document is different. The purpose of this paper is to consider how to select a subset of documents for labeling with a limited budget so that the total cost of the spending does not exceed the budget limit, while at the same time building a classifier with the best classification results.

In this paper, a framework is proposed to select the instances for labeling that integrate two clustering algorithms and two centroid selection methods. From the selected and labeled instances, five different classifiers were constructed with good classification accuracy to prove the superiority of the selected instances.

Experimental results show that this method can establish a training data set containing the most suitable data under the premise of considering the cost constraints. The data set considers both “data representativeness” and “data selection cost,” so that the training data labeled by experts can effectively establish a classifier with high accuracy.

No previous research has considered how to establish a training set with a cost limit when each document has a distinct labeling cost. This paper is the first attempt to resolve this issue.

The paper introduced the use of a hybrid system of neural networks support vector machines (NNSVMs) consisting of artificial neural networks (ANNs) and support vector machines (SVMs) to price single-family properties.

The mechanism of the hybrid system is such that its output is given by the SVMs which utilise the results of the ANNs as their input. The results are compared to other property pricing modelling techniques including the standalone ANNs, SVMs, geographically weighted regression (GWR), spatial error model (SEM), spatial lag model (SLM) and the ordinary least squares (OLS). The techniques were applied to a dataset of 3,225 properties sold during the period, January 2012 to May 2014 in Cape Town, South Africa.

The results demonstrate that the hybrid system performed better than ANNs, SVMs and the OLS. However, in comparison to the spatial models (GWR, SEM and SLM) the hybrid system performed abysmally under with SEM favoured as the best pricing technique.

The findings extend the debate in the body of knowledge that the results of the OLS can significantly be improved through the use of spatial models that correct bias estimates and vary prices across the different property locations. Additionally, utilising the result of the hybrid system is thus affected by the black-box nature of the ANNs and SVMs limiting its use to purposes of checks on estimates predicted by the regression-based models.

The purpose of this study is to analyse the survival of the quantity surveying profession using a strengths, weaknesses, opportunities and threats (SWOT) analysis in an attempt to define the key strengths and weaknesses of the quantity surveying profession based on professional consultants’ experience and to determine the key opportunities and threats which are perceived to impact the quantity surveying profession significantly.

The positivist paradigm was used for this study. The researchers chose quantitative research in the form of questionnaires. A probability sampling method was used. The desired method that was used was the random sampling method. The data were analysed with SPSS using factor analysis and descriptive analysis. A sample of 58 quantity surveyors was randomly selected from the Association of South African Quantity Surveyors (ASAQS) for this study.

Four categories of strengths exist, namely, the ability to plan and solve problems, core quantity surveying measuring skills, project viability and interpersonal skills. Three categories of weaknesses exist, namely, technical inadequacies of quantity surveyors, resistance to change and lack of knowledge of and about the profession. Three categories of opportunities exist, namely, greater demand for quantity surveyors, the need for quantity surveyors and new and existing roles in and out of the profession. Three categories of threats exist, namely, lack of the quantity surveying profession to market itself where new roles require an advancement of core quantity surveying services, external factors that hinder the performance of the profession and the lack of quantity surveying skills in the new generation.

Only members of the ASAQS were included in this research.

The quantity surveying profession requires a strategic framework that will overcome their threats and weaknesses and embrace the strengths and opportunities of the profession to ensure they survive and remain relevant in the ever-changing construction industry. This study aided the quantity surveying profession by identifying the strengths and opportunities and determining the weaknesses and threats faced by the profession.