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1 – 10 of over 2000
Article
Publication date: 9 May 2008

Geng Cui, Man Leung Wong, Guichang Zhang and Lin Li

The purpose of this paper is to assess the performance of competing methods and model selection, which are non‐trivial issues given the financial implications. Researchers…

2625

Abstract

Purpose

The purpose of this paper is to assess the performance of competing methods and model selection, which are non‐trivial issues given the financial implications. Researchers have adopted various methods including statistical models and machine learning methods such as neural networks to assist decision making in direct marketing. However, due to the different performance criteria and validation techniques currently in practice, comparing different methods is often not straightforward.

Design/methodology/approach

This study compares the performance of neural networks with that of classification and regression tree, latent class models and logistic regression using three criteria – simple error rate, area under the receiver operating characteristic curve (AUROC), and cumulative lift – and two validation methods, i.e. bootstrap and stratified k‐fold cross‐validation. Systematic experiments are conducted to compare their performance.

Findings

The results suggest that these methods vary in performance across different criteria and validation methods. Overall, neural networks outperform the others in AUROC value and cumulative lifts, and the stratified ten‐fold cross‐validation produces more accurate results than bootstrap validation.

Practical implications

To select predictive models to support direct marketing decisions, researchers need to adopt appropriate performance criteria and validation procedures.

Originality/value

The study addresses the key issues in model selection, i.e. performance criteria and validation methods, and conducts systematic analyses to generate the findings and practical implications.

Details

Marketing Intelligence & Planning, vol. 26 no. 3
Type: Research Article
ISSN: 0263-4503

Keywords

Article
Publication date: 7 August 2017

Qiangbing Wang, Shutian Ma and Chengzhi Zhang

Based on user-generated content from a Chinese social media platform, this paper aims to investigate multiple methods of constructing user profiles and their effectiveness…

Abstract

Purpose

Based on user-generated content from a Chinese social media platform, this paper aims to investigate multiple methods of constructing user profiles and their effectiveness in predicting their gender, age and geographic location.

Design/methodology/approach

This investigation collected 331,634 posts from 4,440 users of Sina Weibo. The data were divided into two parts, for training and testing . First, a vector space model and topic models were applied to construct user profiles. A classification model was then learned by a support vector machine according to the training data set. Finally, we used the classification model to predict users’ gender, age and geographic location in the testing data set.

Findings

The results revealed that in constructing user profiles, latent semantic analysis performed better on the task of predicting gender and age. By contrast, the method based on a traditional vector space model worked better in making predictions regarding the geographic location. In the process of applying a topic model to construct user profiles, the authors found that different prediction tasks should use different numbers of topics.

Originality/value

This study explores different user profile construction methods to predict Chinese social media network users’ gender, age and geographic location. The results of this paper will help to improve the quality of personal information gathered from social media platforms, and thereby improve personalized recommendation systems and personalized marketing.

Details

The Electronic Library, vol. 35 no. 4
Type: Research Article
ISSN: 0264-0473

Keywords

Book part
Publication date: 30 August 2019

Gary J. Cornwall, Jeffrey A. Mills, Beau A. Sauley and Huibin Weng

This chapter develops a predictive approach to Granger causality (GC) testing that utilizes k…

Abstract

This chapter develops a predictive approach to Granger causality (GC) testing that utilizes k -fold cross-validation and posterior simulation to perform out-of-sample testing. A Monte Carlo study indicates that the cross-validation predictive procedure has improved power in comparison to previously available out-of-sample testing procedures, matching the performance of the in-sample F-test while retaining the credibility of post- sample inference. An empirical application to the Phillips curve is provided evaluating the evidence on GC between inflation and unemployment rates.

Details

Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A
Type: Book
ISBN: 978-1-78973-241-2

Keywords

Book part
Publication date: 16 December 2009

Gaosheng Ju, Rui Li and Zhongwen Liang

In this paper we construct a nonparametric kernel estimator to estimate the joint multivariate cumulative distribution function (CDF) of mixed discrete and continuous…

Abstract

In this paper we construct a nonparametric kernel estimator to estimate the joint multivariate cumulative distribution function (CDF) of mixed discrete and continuous variables. We use a data-driven cross-validation method to choose optimal smoothing parameters which asymptotically minimize the mean integrated squared error (MISE). The asymptotic theory of the proposed estimator is derived, and the validity of the cross-validation method is proved. We provide sufficient and necessary conditions for the uniqueness of optimal smoothing parameters when the estimation of CDF degenerates to the case with only continuous variables, and provide a sufficient condition for the general mixed variables case.

Details

Nonparametric Econometric Methods
Type: Book
ISBN: 978-1-84950-624-3

Abstract

Details

Machine Learning and Artificial Intelligence in Marketing and Sales
Type: Book
ISBN: 978-1-80043-881-1

Article
Publication date: 22 April 2022

Sreedhar Jyothi and Geetanjali Nelloru

Patients having ventricular arrhythmias and atrial fibrillation, that are early markers of stroke and sudden cardiac death, as well as benign subjects are all studied…

Abstract

Purpose

Patients having ventricular arrhythmias and atrial fibrillation, that are early markers of stroke and sudden cardiac death, as well as benign subjects are all studied using the electrocardiogram (ECG). In order to identify cardiac anomalies, ECG signals analyse the heart's electrical activity and show output in the form of waveforms. Patients with these disorders must be identified as soon as possible. ECG signals can be difficult, time-consuming and subject to inter-observer variability when inspected manually.

Design/methodology/approach

There are various forms of arrhythmias that are difficult to distinguish in complicated non-linear ECG data. It may be beneficial to use computer-aided decision support systems (CAD). It is possible to classify arrhythmias in a rapid, accurate, repeatable and objective manner using the CAD, which use machine learning algorithms to identify the tiny changes in cardiac rhythms. Cardiac infractions can be classified and detected using this method. The authors want to categorize the arrhythmia with better accurate findings in even less computational time as the primary objective. Using signal and axis characteristics and their association n-grams as features, this paper makes a significant addition to the field. Using a benchmark dataset as input to multi-label multi-fold cross-validation, an experimental investigation was conducted.

Findings

This dataset was used as input for cross-validation on contemporary models and the resulting cross-validation metrics have been weighed against the performance metrics of other contemporary models. There have been few false alarms with the suggested model's high sensitivity and specificity.

Originality/value

The results of cross validation are significant. In terms of specificity, sensitivity, and decision accuracy, the proposed model outperforms other contemporary models.

Details

International Journal of Intelligent Unmanned Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2049-6427

Keywords

Article
Publication date: 3 July 2009

Shi‐Woei Lin and Chih‐Hsing Cheng

The purpose of this paper is to compare various linear opinion pooling models for aggregating probability judgments and to determine whether Cooke's performance weighting…

628

Abstract

Purpose

The purpose of this paper is to compare various linear opinion pooling models for aggregating probability judgments and to determine whether Cooke's performance weighting model can sift out better calibrated experts and produce better aggregated distribution.

Design/methodology/approach

The leave‐one‐out cross‐validation technique is adopted to perform an out‐of‐sample comparison of Cooke's classical model, the equal weight linear pooling method, and the best expert approach.

Findings

Both aggregation models significantly outperform the best expert approach, indicating the need for inputs from multiple experts. The performance score for Cooke's classical model drops considerably in out‐of‐sample analysis, indicating that Cooke's performance weight approach might have been slightly overrated before, and the performance weight aggregation method no longer dominantly outperforms the equal weight linear opinion pool.

Research limitations/implications

The results show that using seed questions to sift out better calibrated experts may still be a feasible approach. However, because the superiority of Cooke's model as discussed in previous studies can no longer be claimed, whether the cost of extra efforts used in generating and evaluating seed questions is justifiable remains a question.

Originality/value

Understanding the performance of various models for aggregating experts' probability judgments is critical for decision and risk analysis. Furthermore, the leave‐one‐out cross‐validation technique used in this study achieves more objective evaluations than previous studies.

Details

Journal of Modelling in Management, vol. 4 no. 2
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 5 June 2017

Eugene Yujun Fu, Hong Va Leong, Grace Ngai and Stephen C.F. Chan

Social signal processing under affective computing aims at recognizing and extracting useful human social interaction patterns. Fight is a common social interaction in…

Abstract

Purpose

Social signal processing under affective computing aims at recognizing and extracting useful human social interaction patterns. Fight is a common social interaction in real life. A fight detection system finds wide applications. This paper aims to detect fights in a natural and low-cost manner.

Design/methodology/approach

Research works on fight detection are often based on visual features, demanding substantive computation and good video quality. In this paper, the authors propose an approach to detect fight events through motion analysis. Most existing works evaluated their algorithms on public data sets manifesting simulated fights, where the fights are acted out by actors. To evaluate real fights, the authors collected videos involving real fights to form a data set. Based on the two types of data sets, the authors evaluated the performance of their motion signal analysis algorithm, which was then compared with the state-of-the-art approach based on MoSIFT descriptors with Bag-of-Words mechanism, and basic motion signal analysis with Bag-of-Words.

Findings

The experimental results indicate that the proposed approach accurately detects fights in real scenarios and performs better than the MoSIFT approach.

Originality/value

By collecting and annotating real surveillance videos containing real fight events and augmenting with well-known data sets, the authors proposed, implemented and evaluated a low computation approach, comparing it with the state-of-the-art approach. The authors uncovered some fundamental differences between real and simulated fights and initiated a new study in discriminating real against simulated fight events, with very good performance.

Details

International Journal of Pervasive Computing and Communications, vol. 13 no. 2
Type: Research Article
ISSN: 1742-7371

Keywords

Book part
Publication date: 23 June 2016

Daniel J. Henderson and Christopher F. Parmeter

It is known that model averaging estimators are useful when there is uncertainty governing which covariates should enter the model. We argue that in applied research there…

Abstract

It is known that model averaging estimators are useful when there is uncertainty governing which covariates should enter the model. We argue that in applied research there is also uncertainty as to which method one should deploy, prompting model averaging over user-defined choices. Specifically, we propose, and detail, a nonparametric regression estimator averaged over choice of kernel, bandwidth selection mechanism and local-polynomial order. Simulations and an empirical application are provided to highlight the potential benefits of the method.

Details

Essays in Honor of Aman Ullah
Type: Book
ISBN: 978-1-78560-786-8

Keywords

Abstract

Details

Applied Structural Equation Modelling for Researchers and Practitioners
Type: Book
ISBN: 978-1-78635-882-0

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