Organizational learning II: Theory, method and practice

A wrapper method for feature selection using Support Vector Machines

Feature selection is of considerable importance in data mining and machine learning, especially for high dimensional data. In this paper, we propose a novel nearest neighbor-based feature weighting algorithm, which learns a feature weighting vector by maximizing the expected leave-one-out classification accuracy with a regularization term. The algorithm makes no parametric assumptions about the distribution of the data and scales naturally to multiclass problems. Experiments conducted on artificial and real data sets demonstrate that the proposed algorithm is largely insensitive to the increase in the number of irrelevant features and performs better than the state-of-the-art methods in most cases.

Neighborhood Component Feature Selection for High-Dimensional Data

Purpose




The European Journal of Marketing was created in 1967. In 2017, the journal celebrates its 50th anniversary. Therefore, the purpose of this study is to present a bibliometric overview of the leading trends of the journal during this period.




Design/methodology/approach




This work uses the Scopus database to analyse the most productive authors, institutions and countries, as well as the most cited papers and the citing articles. The investigation uses bibliometric indicators to represent the bibliographic data, including the total number of publications and citations between 1967 and 2017. Additionally, the article also develops a graphical visualization of the bibliographic material by using the visualization of similarities viewer software to map journals, keywords and institutions with bibliographic coupling and co-citation analysis.




Findings




British authors and institutions are the most productive in the journal, although Australians’ are growing significantly the number of papers published. Continental European institutions are also increasing the number of publications, but they are still far from reaching the British contribution so far. In the mid-term, however, these zone’s authors and institutions, especially those from big European countries like France, Germany, Italy and Spain, should reach a closer performance to British ones; more as less long, historic, but more recent periods of analysis are considered.




Practical implications




This article is useful for any reader of this journal to understand questions such as papers’ European Journal of Marketing-related scientific productivity in terms of, for instance, contributors/authors, institutions and countries, or the main sources used to back them.




Originality/value




This is the first comprehensive article offering a general overview of the leading trends and researchers of the journal over its history.

Fifty years of the European Journal of Marketing: a bibliometric analysis

Industry 4.0: A bibliometric analysis and detailed overview

Forty years of the European Journal of Operational Research: A bibliometric overview

Since 2005, Chile's professional soccer league has used a game-scheduling system that is based on an integer linear programming model. The Chilean league managers considered several operational, economic, and sporting criteria for the final tournaments' scheduling. Thus, they created a highly constrained problem that had been, in practice, unsolvable using their previous methodology. This led to the adoption of a model that used some techniques that were new in soccer-league sports scheduling. The schedules they generated provided the teams with benefits such as lower costs, higher incomes, and fairer seasons. In addition, the tournaments were more attractive to sports fans. The success of the new scheduling system has completely fulfilled the expectations of the Asociacion Nacional de Futbol Profesional (ANFP), the organization for Chilean professional soccer.

/pdf/scheduling-the-chilean-soccer-league-by-integer-programming-3tlqxev3em.pdf

Scheduling the Chilean Soccer League by Integer Programming

Forty years of Safety Science: A bibliometric overview

Traditional methodologies for time series prediction take the series to be predicted and split it into training, validation, and test sets. The first one serves to construct forecasting models, the second set for model selection, and the third one is used to evaluate the final model. Different time series approaches such as ARIMA and exponential smoothing, as well as regression techniques such as neural networks and support vector regression, have been successfully used to develop forecasting models. A problem that has not yet received proper attention, however, is how to update such forecasting models when new data arrives, i.e. when a new event of the considered time series occurs. This paper presents a strategy to update support vector regression based forecasting models for time series with seasonal patterns. The basic idea of this updating strategy is to add the most recent data to the training set every time a predefined number of observations takes place. This way, information in new data is taken into account in model construction. The proposed strategy outperforms the respective static version in almost all time series studied in this work, considering three different error measures.

/pdf/a-model-updating-strategy-for-predicting-time-series-with-4kc7why71c.pdf

A model updating strategy for predicting time series with seasonal patterns

Uplift modeling is an approach for estimating the incremental effect of an action or treatment at the individual level. It has gained attention in the marketing and analytics communities due to its ability to adequately model the effect of direct marketing actions via predictive analytics. The main contribution of our study is the implementation of the uplift modeling framework to maximize the effectiveness of retention efforts in higher education institutions i.e., improvement of academic performance by offering tutorials. The objective is to improve the design of retention programs by tailoring them to students who are more likely to be retained if targeted. Data from three different bachelor programs from a Chilean university were collected. Students who participated in the tutorials are considered the treatment group, otherwise, they are assigned to the nontreatment group. Our results demonstrate the virtues of uplift modeling in tailoring retention efforts in higher education over conventional predictive modeling approaches.

Jaime Miranda

Papers

Forty years of the European Journal of Operational Research: A bibliometric overview

Scheduling the Chilean Soccer League by Integer Programming

Forty years of Safety Science: A bibliometric overview

A model updating strategy for predicting time series with seasonal patterns

Uplift Modeling for preventing student dropout in higher education