A Non-sequential Representation of Sequential Data for Churn Prediction

Eastwood, Mark; Gabrys, Bogdan

doi:10.1007/978-3-642-04595-0_26

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…The task is usually to derive the maximum likelihood estimate of the parameters of the HMM for the set of output sequences. Typical physical activities are nonsequential, and it is not easy to use HMM to recognize a single physical activity [28].…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework

Zheng

2015

Journal of Electrical and Computer Engineering

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Human activity recognition via triaxial accelerometers can provide valuable information for evaluating functional abilities. In this paper, we present an accelerometer sensor-based approach for human activity recognition. Our proposed recognition method used a hierarchical scheme, where the recognition of ten activity classes was divided into five distinct classification problems. Every classifier used the Least Squares Support Vector Machine (LS-SVM) and Naive Bayes (NB) algorithm to distinguish different activity classes. The activity class was recognized based on the mean, variance, entropy of magnitude, and angle of triaxial accelerometer signal features. Our proposed activity recognition method recognized ten activities with an average accuracy of 95.6% using only a single triaxial accelerometer.

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Section: Introductionmentioning

confidence: 99%

Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework

Zheng

2015

Journal of Electrical and Computer Engineering

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“…The only configuration possible is a choice of different values for K ≥ 1, that is, how many nearest neighbours we want to use. KNNs can be used in its original non‐sequential form (Eastwood & Gabrys, ), or it can be extended into a sequential approach (Ruta et al ., ). The core idea is to find similar sequences expecting that these sequences have a common behaviour and outcome.…”

Section: Predictive Modelsmentioning

confidence: 99%

A hybrid model for business process event and outcome prediction

Gabrys

Nauck

2014

Expert Systems

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Large service companies run complex customer service processes to provide communication services to their customers. The flawless execution of these processes is essential because customer service is an important differentiator. They must also be able to predict if processes will complete successfully or run into exceptions in order to intervene at the right time, preempt problems and maintain customer service. Business process data are sequential in nature and can be very diverse. Thus, there is a need for an efficient sequential forecasting methodology that can cope with this diversity. This paper proposes two approaches, a sequential k nearest neighbour and an extension of Markov models both with an added component based on sequence alignment. The proposed approaches exploit temporal categorical features of the data to predict the process next steps using higher order Markov models and the process outcomes using sequence alignment technique. The diversity aspect of the data is also added by considering subsets of similar process sequences based on k nearest neighbours. We have shown, via a set of experiments, that our sequential k nearest neighbour offers better results when compared with the original ones; our extension Markov model outperforms random guess, Markov models and hidden Markov models.

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“…One modern and powerful classifier is the support vector machine (SVM), which has the advantages of being robust and delivering a unique solution because the optimality problem is convex (Auria and Moro 2008). A SVM provides more accurate activity recognition compared to other classifiers such as a hidden Markov model (Eastwood and Gabrys et al 2009) or a neural network (Lara and Labrador et al 2013). However, a SVM is expensive computationally, especially for large datasets.…”

Section: Introductionmentioning

confidence: 99%