Review on wrapper feature selection approaches

Aboudi, Naoual El; Benhlima, Laïla

doi:10.1109/icemis.2016.7745366

Cited by 126 publications

(73 citation statements)

References 13 publications

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“…Dataset format that supports LIBSVM is illustrated in Figure 4. Furthermore, ILFS and Relief are implemented using feature selection library called FSLib [10], [21].…”

Section: B Methodsmentioning

confidence: 99%

“…The weights can be calculated with probability co-occurrences between features and tokens using PLSA technique [16] Finally, the weight is optimized using the Expectation-Maximization (EM) algorithm. Further details about ILFS can refer to [10]. The detailed process of ILFS technique is described as follow:…”

Section: B Infinite Latent Feature Selection (Ilfs)mentioning

confidence: 99%

“…The dataset in a hyperspectral cube format is converted into a map that correspondence between its pixel and the reflectance value to make feature extraction process easier. The next step is to implement a technique feature selection with Infinite Latent Feature Selection (ILFS) [10] to select the most influential feature in the surface recognition process. The results are then used as input for the learning and testing process using a Support Vector Machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

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Infinite Latent Feature Selection Technique for Hyperspectral Image Classification

Miftahushudur

Wael

Praludi

2019

indones.j.electron.telecommun.

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The classification process is one of the most crucial processes in hyperspectral imaging. One of the limitations in classification process using machine learning technique is its complexities, where hyperspectral image format has a thousand band that can be used as a feature for learning purpose. This paper presents a comparison between two feature selection technique based on probability approach that not only can tackle the problem, but also improve accuracy. Infinite Latent Feature Selection (ILFS) and Relief Techniques are implemented in a hyperspectral image to select the most important feature or band before applied in Support Vector Machine (SVM). The result showed ILFS technique can improve classification accuracy better than Relief (92.21% vs. 88.10%). However, Relief can extract less feature to reach its best accuracy with only 6 features compared with ILFS with 9.

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“…Dataset format that supports LIBSVM is illustrated in Figure 4. Furthermore, ILFS and Relief are implemented using feature selection library called FSLib [10], [21].…”

Section: B Methodsmentioning

confidence: 99%

Section: B Infinite Latent Feature Selection (Ilfs)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Infinite Latent Feature Selection Technique for Hyperspectral Image Classification

Miftahushudur

Wael

Praludi

2019

indones.j.electron.telecommun.

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“…On the contrary, if l approaches t the system may miss an occupied room. We Algorithm 1 Location-Aware HMM Z ← Z∪ Viterbi(HMM) 9: end for have empirically seen that for l = 1 3 * t (line 3), we get the best accuracy. Given the feature set x t of the occupied rooms, the algorithm calculates the new emission probability (line 4).…”

Section: Occupancy Estimation Algorithmmentioning

confidence: 99%

Indoor Occupancy Estimation via Location-Aware HMM: An IoT Approach

Yoshida

Kleisarchaki

Gtirgen

et al. 2018

2018 IEEE 19th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

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Indoor occupancy estimation is a critical analytical task for several applications (e.g., social isolation of elderlies). The proliferation of Internet of Things (IoT) devices enabled the occupancy estimation, as it provided access to a mass amount of data. Several works have been proposed exploiting the IoT Passive Inference (PIR) or environmental (e.g., CO2) features. These works however are traditionally selecting the feature space at the learning phase and passively using it over time. Hence, they ignore the dynamics of indoor occupancy, such as the location of the occupant or his motion patterns, leading to a decreasing accuracy over time. In this paper, we study those dynamics and show that motion patterns, along with environmental features favor the occupancy estimation. We design a Location-Aware Hidden Markov Model (HMM), which dynamically adapts the feature space based on the occupant's location. Our experiments on real data show that Location-Aware HMM can reach up to 10% better accuracy than Conventional HMM.

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“…Feature selection techniques are important to select features for good classification results. Previous works on feature selection were based on filter or wrapper methods [26][27][28]. However, the methods need to process a large number of data to find out appropriate features, which is hard to achieve in real-time and low-cost platform.…”

Section: Feature Selection and Extractionmentioning

confidence: 99%

A Novel Low-Cost Real-Time Power Measurement Platform for LoWPAN IoT Devices

Liu¹,

Wang²,

Gao³

et al. 2017

Mobile Information Systems

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With the rapid development of technology and application for Internet of Things (IoT), Low-Power Wireless Personal Area Network (LoWPAN) devices are more popularly applied. Evaluation of power efficiency is important to LoWPAN applications. Conventional method to evaluate the power efficiency of different LoWPAN devices is as follows: first measure the current of the devices under working/idle/sleep state and then make an average and estimation of the lifetime of batteries, which deeply relied on the accuracy of testing equipment and is not that accurate and with high cost. In this work, a low-cost, real-time power measurement platform called PTone is proposed, which can be used to detect the real-time power of LoWPAN devices (above 99.63%) and be able to determine the state of each module of DUT system. Based on the PTone, a novel abnormal status diagnosis mechanism is proposed. The mechanism can not only judge abnormal status but also find accurate abnormal status locating and classify abnormal status accurately. According to the method, each state of Device Under Test (DUT) during wireless transmission is estimated, different abnormal status can be classified, and thus specific location of abnormal module can be found, which will significantly shorten the development process for LoWPAN devices and thus reduce costs.

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Review on wrapper feature selection approaches

Cited by 126 publications

References 13 publications

Infinite Latent Feature Selection Technique for Hyperspectral Image Classification

Infinite Latent Feature Selection Technique for Hyperspectral Image Classification

Indoor Occupancy Estimation via Location-Aware HMM: An IoT Approach

A Novel Low-Cost Real-Time Power Measurement Platform for LoWPAN IoT Devices

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