Using self-organizing maps to model turnover of sales agents in a call center

Valle, Mauricio A.; Ruz, Gonzalo A.; Masías, Víctor Hugo

doi:10.1016/j.asoc.2017.03.011

Cited by 16 publications

(11 citation statements)

References 34 publications

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“…The models of these algorithms are not trained on previously classified data, such that the algorithms are bound to find patterns within the processed data. Wellknown algorithms in the context of clustering are for example K-Means (Shahrivari and Jalili 2016), DBScan (Junior and da Silva 2017), Self-Organizing Maps (SOM) (Valle et al 2017), BIRCH (Lorbeer et al 2018) and Topic model (Gong et al 2018). Another subfield that is often regarded as part of unsupervised learning is dimensionality reduction.…”

Section: Principal Component Analysismentioning

confidence: 99%

Machine learning in information systems - a bibliographic review and open research issues

2021

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Artificial Intelligence (AI) and Machine Learning (ML) are currently hot topics in industry and business practice, while management-oriented research disciplines seem reluctant to adopt these sophisticated data analytics methods as research instruments. Even the Information Systems (IS) discipline with its close connections to Computer Science seems to be conservative when conducting empirical research endeavors. To assess the magnitude of the problem and to understand its causes, we conducted a bibliographic review on publications in high-level IS journals. We reviewed 1,838 articles that matched corresponding keyword-queries in journals from the AIS senior scholar basket, Electronic Markets and Decision Support Systems (Ranked B). In addition, we conducted a survey among IS researchers (N = 110). Based on the findings from our sample we evaluate different potential causes that could explain why ML methods are rather underrepresented in top-tier journals and discuss how the IS discipline could successfully incorporate ML methods in research undertakings.

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Section: Principal Component Analysismentioning

confidence: 99%

Machine learning in information systems - a bibliographic review and open research issues

2021

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“…Moreover, a feature vector is composed by a set of D calculated features from the available physical magnitudes, and the obtained feature vectors are represented into an D-dimensional space. Indeed, the information included in such D-dimensional space mostly has nonlinear structures; thereby, in order to overcome such drawbacks, manifold learning methods, as SOM, are being developed as superior approaches in the last years [37]- [38].…”

Section: Data-driven Condition Monitoringmentioning

confidence: 99%

Multiple-fault detection and identification scheme based on hierarchical self-organizing maps applied to an electric machine

Saucedo-Dorantes

Delgado-Prieto

Romero-Troncoso

et al. 2019

Applied Soft Computing

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“…Speech signals play an important role to identify the mental state of the speakers by recognizing their emotions during his/her speech 6 . The SER systems are widely used in different applications to identify and recognize the physical conditions, such as biometric studies, 7 call centers, 8 and healthcare systems 9 that utilize speech signals. Nowadays, the SER models are involved and preferred in human–machine interaction (HMI) to transfer the physical abilities and the natural abilities of humans to computer systems 10 .…”

Section: Introductionmentioning

confidence: 99%

Optimal feature selection based speech emotion recognition using two‐stream deep convolutional neural network

Mustaqeem

Kwon

2021

Int J of Intelligent Sys

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Speech signal processing is an active area of research, the most dominant source of exchanging information among human beings, and the best way for human–computer interaction (HCI). Human behavior assessments and emotion recognition from a speech signal, such as speech emotion recognition (SER) is an emerging HCI area of exploration with various real time claims. The performance of an efficient SER system depends on feature learning, which include salient and discriminative information such as high‐level deep features. In this paper, we proposed a two‐stream deep convolutional neural network with an iterative neighborhood component analysis (INCA) to learn mutually spatial‐spectral features and select the most discriminative optimal features for the final prediction. Our model is composed of two channels, and each channel is associated with the convolutional neural network structure to extract cues from the oral signals. The first channel extracts feature from the spectral domain, and the second channel extracts features from the spatial domain, which are then fused and fed to the INCA to remove the severance and select the optimal features for the final model training. The joint refine features are passed from the fully connected network with a softmax classifier to yield the predictions of the different emotions. We trained our proposed system using three benchmarks, which included the EMO‐DB, SAVEE, and RAVDESS emotional speech corpora, and we tested the prediction performance to secure 95%, 82%, and 85% recognition rates. The performance of the system shows the effectiveness and significance of the proposed system.

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Using self-organizing maps to model turnover of sales agents in a call center

Cited by 16 publications

References 34 publications

Machine learning in information systems - a bibliographic review and open research issues

Machine learning in information systems - a bibliographic review and open research issues

Multiple-fault detection and identification scheme based on hierarchical self-organizing maps applied to an electric machine

Optimal feature selection based speech emotion recognition using two‐stream deep convolutional neural network

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