A non-parametric mixture of Gaussian naive Bayes classifiers based on local independent features

Jahromi, Ali Haghpanah; Taheri, Mohammad

doi:10.1109/aisp.2017.8324083

Cited by 143 publications

(60 citation statements)

References 9 publications

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“…This probabilistic classifier presents strong independence assumptions between the variables/features given the class. Furthermore, this model embraces the assumption that the data follow the Gaussian distribution [36,37];  Random forest (RF) belongs in the ensemble learning methods and is based on decision trees. This model constructs a large number of decision trees.…”

Section: Algorithm 1 Pseudoalgorithm Of the Clustering Processmentioning

confidence: 99%

Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients

Ntakolia

Kokkotis

Moustakidis³

et al. 2021

Diagnostics

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Osteoarthritis is a joint disease that commonly occurs in the knee (KOA). The continuous increase in medical data regarding KOA has triggered researchers to incorporate artificial intelligence analytics for KOA prognosis or treatment. In this study, two approaches are presented to predict the progression of knee joint space narrowing (JSN) in each knee and in both knees combined. A machine learning approach is proposed with the use of multidisciplinary data from the osteoarthritis initiative database. The proposed methodology employs: (i) A clustering process to identify groups of people with progressing and non-progressing JSN; (ii) a robust feature selection (FS) process consisting of filter, wrapper, and embedded techniques that identifies the most informative risk factors; (iii) a decision making process based on the evaluation and comparison of various classification algorithms towards the selection and development of the final predictive model for JSN; and (iv) post-hoc interpretation of the features’ impact on the best performing model. The results showed that bounding the JSN progression of both knees can result to more robust prediction models with a higher accuracy (83.3%) and with fewer risk factors (29) compared to the right knee (77.7%, 88 risk factors) and the left knee (78.3%, 164 risk factors), separately.

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Section: Algorithm 1 Pseudoalgorithm Of the Clustering Processmentioning

confidence: 99%

Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients

Ntakolia

Kokkotis

Moustakidis³

et al. 2021

Diagnostics

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“…Naïve Bayes' algorithm; which gives the probability of the prediction, in knowledge of previous events [77][78][79][80][81][82]. Clustering is always using mathematics; we will group the data into packets so that in each packet the data is as close as possible to each other [83][84][85]. It is used in particular in recommendations for films close to the films we have already seen!…”

Section: Machine Learningmentioning

confidence: 99%

Artificial Intelligence Approach for Target Classification: A State of The Art

Abdellaoui¹,

Daghouj²,

Fattah³

et al. 2020

Adv. sci. technol. eng. syst. j.

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The classification of static or mobile objects, from a signal or an image containing information as to their structure or their form, constitutes a constant concern of specialists in the electronic field. The remarkable progress made in past years, particularly in the development of neural networks and artificial intelligence systems, has further accentuated this trend. The fields of application and potential uses of Artificial Intelligence are increasingly diverse: understanding of natural language, visual recognition, robotics, autonomous system, Machine learning, etc. This paper is a state of the art on the classification of radar signals. It focuses on the contribution of artificial intelligence to the latter without forgetting target tracking. This by evoking the different feature extractors, classifiers and the existing identification deep learning algorithms. We detail also the process allowing carrying out this classification.

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“…In addition to linear classification, it is possible to use SVM to effectively perform nonlinear classification using the kernel method, implicitly mapping their inputs into a multi-dimensional feature space. NBs belong to a probabilistic family of classifiers [32]. They are centered on the theorem of Bayes, which is based on the assumptions of independence among features.…”

Section: Background On Machine Learningmentioning

confidence: 99%

A Feature-Based Model for the Identification of Electrical Devices in Smart Environments

Tundis

Faizan

Mühlhäuser

2019

Sensors

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Smart Homes (SHs) represent the human side of a Smart Grid (SG). Data mining and analysis of energy data of electrical devices in SHs, e.g., for the dynamic load management, is of fundamental importance for the decision-making process of energy management both from the consumer perspective by saving money and also in terms of energy redistribution and reduction of the carbon dioxide emission, by knowing how the energy demand of a building is composed in the SG. Advanced monitoring and control mechanisms are necessary to deal with the identification of appliances. In this paper, a model for their automatic identification is proposed. It is based on a set of 19 features that are extracted by analyzing energy consumption, time usage and location from a set of device profiles. Then, machine learning approaches are employed by experimenting different classifiers based on such model for the identification of appliances and, finally, an analysis on the feature importance is provided.

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A non-parametric mixture of Gaussian naive Bayes classifiers based on local independent features

Cited by 143 publications

References 9 publications

Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients

Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients

Artificial Intelligence Approach for Target Classification: A State of The Art

A Feature-Based Model for the Identification of Electrical Devices in Smart Environments

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