Manipulating Data and Dimension Reduction Methods: Feature Selection

Huan, Liu; Zhao, Zheng

doi:10.1007/978-1-4614-1800-9_115

Cited by 32 publications

(20 citation statements)

References 17 publications

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“…Feature selection is an important technology of feature engineering, 24,25 which can automatically remove repetitive, redundant, and irrelevant features from the data and filter out the attributes most closely related to the target problem to construct a subset of features, thus reducing the complexity of the model and enhancing interpretation. The methods of feature selection can be divided into: filtering method, packaging method, and embedding method 26,27 . In this paper, the filtering method was adopted.…”

Section: Theoretical Framework Of the Methods Usedmentioning

confidence: 99%

“…The methods of feature selection can be divided into: filtering method, packaging method, and embedding method. 26,27 In this paper, the filtering method was adopted. First, the variance analysis was carried out on the attribute values of positive and negative samples, and the F statistic was used to judge whether each factor had a significant influence on pregnancy outcome, and the nonsignificant indicators were eliminated.…”

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A machine learning‐based risk scoring system for infertility considering different age groups

et al. 2020

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Section: Theoretical Framework Of the Methods Usedmentioning

confidence: 99%

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confidence: 99%

A machine learning‐based risk scoring system for infertility considering different age groups

et al. 2020

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“…The task is the optimization process of finding the optimal subset of features that offer the best performance for machine learning algorithms. A variety of optimization algorithms have been applied to feature selection, including complete search, greedy search, heuristic search, and random search [44], [45], [46], [47]. However, most of existing feature selection methods are prone to stagnation in local optima [48].…”

Section: Quantum Evolutionary Algorithmsmentioning

confidence: 99%

What an “Ehm” Leaks About You: Mapping Fillers into Personality Traits with Quantum Evolutionary Feature Selection Algorithms

Tayarani

Esposito

Vinciarelli

2022

IEEE Trans. Affective Comput.

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Tayarani, M., Esposito, A. and Vinciarelli, A. (2019) What an "ehm" leaks about you: mapping fillers into personality traits with quantum evolutionary feature selection algorithms. IEEE Transactions on Affective Computing, (Abstract-This work shows that fillers -short utterances like "ehm" and "uhm" -allow one to predict whether someone is above median along the Big-Five personality traits. The experiments have been performed over a corpus of 2,988 fillers uttered by 120 different speakers in spontaneous conversations. The results show that the prediction accuracies range between 74% and 82% depending on the particular trait. The proposed approach includes a feature selection step -based on Quantum Evolutionary Algorithms -that has been used to detect the personality markers, i.e., the subset of the features that better account for the prediction outcomes and, indirectly, for the personality of the speakers. The results show that only a relatively few features tend to be consistently selected, thus acting as reliable personality markers.

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“…FS helps remove irrelevant or redundant features thus it reduces the energy consumption in later process. Liu [ 46 ] concludes categories of FS algorithms into two types, supervised algorithms and unsupervised algorithms. In [ 47 ], an overview of existing FS algorithms such as filter, wrapper and embedded methods is described.…”

Section: Key Techniquesmentioning

confidence: 99%

A Survey of Data Semantization in Internet of Things

Shi

Zhu

2018

Sensors

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With the development of Internet of Things (IoT), more and more sensors, actuators and mobile devices have been deployed into our daily lives. The result is that tremendous data are produced and it is urgent to dig out hidden information behind these volumous data. However, IoT data generated by multi-modal sensors or devices show great differences in formats, domains and types, which poses challenges for machines to process and understand. Therefore, adding semantics to Internet of Things becomes an overwhelming tendency. This paper provides a systematic review of data semantization in IoT, including its backgrounds, processing flows, prevalent techniques, applications, existing challenges and open issues. It surveys development status of adding semantics to IoT data, mainly referring to sensor data and points out current issues and challenges that are worth further study.

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Manipulating Data and Dimension Reduction Methods: Feature Selection

Cited by 32 publications

References 17 publications

A machine learning‐based risk scoring system for infertility considering different age groups

A machine learning‐based risk scoring system for infertility considering different age groups

What an “Ehm” Leaks About You: Mapping Fillers into Personality Traits with Quantum Evolutionary Feature Selection Algorithms

A Survey of Data Semantization in Internet of Things

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