Construction of a classifier with prior domain knowledge formalised as Bayesian network

Antal, Péter

doi:10.1109/iecon.1998.724126

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Thus, given y be a certain class and x i ...x n the data, Naive Bayes classifier based on the Bayesian rules and the likelihood splits in the product of the conditional probabilities given the class P(y|x 1 ... Naive Bayes has been applied in activity recognition because of the simple assumption on the likelihood, which is usually violated in practice [29,77,81,86] Adaboost is part of the classifier ensembles. Classifier ensembles encompass all algorithms that combine different classifiers together.…”

Section: Traditional Machine Learningmentioning

confidence: 99%

Trends in human activity recognition using smartphones

Ferrari

Micucci

Mobilio

et al. 2021

J Reliable Intell Environ

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Recognizing human activities and monitoring population behavior are fundamental needs of our society. Population security, crowd surveillance, healthcare support and living assistance, and lifestyle and behavior tracking are some of the main applications that require the recognition of human activities. Over the past few decades, researchers have investigated techniques that can automatically recognize human activities. This line of research is commonly known as Human Activity Recognition (HAR). HAR involves many tasks: from signals acquisition to activity classification. The tasks involved are not simple and often require dedicated hardware, sophisticated engineering, and computational and statistical techniques for data preprocessing and analysis. Over the years, different techniques have been tested and different solutions have been proposed to achieve a classification process that provides reliable results. This survey presents the most recent solutions proposed for each task in the human activity classification process, that is, acquisition, preprocessing, data segmentation, feature extraction, and classification. Solutions are analyzed by emphasizing their strengths and weaknesses. For completeness, the survey also presents the metrics commonly used to evaluate the goodness of a classifier and the datasets of inertial signals from smartphones that are mostly used in the evaluation phase.

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Section: Traditional Machine Learningmentioning

confidence: 99%

Trends in human activity recognition using smartphones

Ferrari

Micucci

Mobilio

et al. 2021

J Reliable Intell Environ

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“…Bayesian network shows the dependence-independence relations in an understandable form that renders the tasks of decomposition, feature selection, or transformation more principled [3], besides providing a sound inference mechanism. However, Bayesian Network requires a priori knowledge of many probabilities, which are usually estimated based on assumptions about the form of the underlying distributions.…”

Section: Introductionmentioning

confidence: 99%

Addressing the problems of Bayesian network classification of video using high-dimensional features

Mittal

Cheong

2004

IEEE Trans. Knowl. Data Eng.

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Abstract-Bayesian theory is of great interest in pattern classification. In this paper, we present an approach to aid in the effective application of Bayesian networks in tasks like video classification, where descriptors originate from varied sources and are large in number. In order to extend the application of conventional Bayesian theory to the case of continuous and nonparametric descriptor space, dimension partitioning into attributes by minimizing the discrete Bayes error is proposed. The partitioning output goes to the dimensionality reduction module. A new algorithm for dimensionality reduction for improving the classification accuracy is proposed based on the class pair discriminative capacity of the dimensions. It is also shown how attributes can be weighed automatically in a single-label assignment based on comparing the class pairs. A computationally efficient method to assign multiple labels on the samples is also presented. Comparison with standard classification tools on video data of more than 4,000 segments shows the potential of our approach in pattern classification.

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