Adaptive, Hybrid Feature Selection (AHFS)

Viharos, Zsolt János; Kis, Krisztián Balázs; Fodor, Ádám; Büki, Máté István

doi:10.1016/j.patcog.2021.107932

Cited by 26 publications

(8 citation statements)

References 22 publications

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“…In order to validate our results, another approach was also used by our team members, called Adaptive, Hybrid Feature Selection or AHFS. This hybrid solution combines the existing (supervised, state-of-the-art) feature selection techniques, which have their own specific but fixed feature evaluation measures/metrics, in an adaptive manner [ 62 ]. The AHFS algorithm is a simple method that first selects the “most important” feature, then another, the third, and so on.…”

Section: Discussionmentioning

confidence: 99%

The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder

Nagy

Dombi

Fülep

et al. 2023

Sensors

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(1) Background and Goal: Several studies have investigated the association of sleep, diurnal patterns, and circadian rhythms with the presence and with the risk states of mental illnesses such as schizophrenia and bipolar disorder. The goal of our study was to examine actigraphic measures to identify features that can be extracted from them so that a machine learning model can detect premorbid latent liabilities for schizotypy and bipolarity. (2) Methods: Our team developed a small wrist-worn measurement device that collects and identifies actigraphic data based on an accelerometer. The sensors were used by carefully selected healthy participants who were divided into three groups: Control Group (C), Cyclothymia Factor Group (CFG), and Positive Schizotypy Factor Group (PSF). From the data they collected, our team performed data cleaning operations and then used the extracted metrics to generate the feature combinations deemed most effective, along with three machine learning algorithms for categorization. (3) Results: By conducting the training, we were able to identify a set of mildly correlated traits and their order of importance based on the Shapley value that had the greatest impact on the detection of bipolarity and schizotypy according to the logistic regression, Light Gradient Boost, and Random Forest algorithms. (4) Conclusions: These results were successfully compared to the results of other researchers; we had a similar differentiation in features used by others, and successfully developed new ones that might be a good complement for further research. In the future, identifying these traits may help us identify people at risk from mental disorders early in a cost-effective, automated way.

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Section: Discussionmentioning

confidence: 99%

The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder

Nagy

Dombi

Fülep

et al. 2023

Sensors

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“…Figure 2: Applying the waveform toolpath, the similar behaviour between the measured vibration signal based, selected engineering feature and the change in the micro-milling tool wear described by the changes in the volume of the machined pockets [16] Figure 2 shows the almost identical change in the micro-milling tool wear described by the changes in the volume of the machined pockets and the vibration based, calculated variable appointed by the feature selection method [17]. Based on the graph, one can recognize that the two diagrams behave almost identical, consequently, the actual status of the tool lifetime can be well monitored with vibration-based diagnostics method in micro-milling of ceramics.…”

Section: Figure 1: Element Of the Waveform Path [6]mentioning

confidence: 99%

Vibration based characterization of tool wearing in micro-milling of ceramics during waveform toolpath

Móricz¹,

Viharos²

2022

Gradus

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The main aim of the paper is to monitor the micro- milling tool wearing process offline, by a microscopic tool measurement on one hand, and using high-frequency vibration measurement online, during the cutting process, on the other. Relations between the rake face wear stages and the measured online and offline parameters were determined applying an owndeveloped, artificial neural network based feature selection solution. As the main result, the experiment-based research appoints that the measured vibration variable component(s) which characterizes the key, three tool wearing phases, in the same way as the real rake face wearing stages occur, applying the waveform tool path

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“…Hybrid methods are generally adopted in feature selection techniques [12]. Researchers have developed new adaptive hybrid feature selection technique by combining different individual methods to obtain a generalized solution [27].…”

Section: Literature Reviewmentioning

confidence: 99%

A two-phase feature selection technique using mutual information and XGB-RFE for credit card fraud detection

Priscilla¹,

Prabha²

2021

IJATEE

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Adaptive, Hybrid Feature Selection (AHFS)

Cited by 26 publications

References 22 publications

The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder

The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder

Vibration based characterization of tool wearing in micro-milling of ceramics during waveform toolpath

A two-phase feature selection technique using mutual information and XGB-RFE for credit card fraud detection

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