Speeding up incremental wrapper feature subset selection with Naive Bayes classifier

Bermejo, Pablo; Gámez, José A.; Puerta, José M.

doi:10.1016/j.knosys.2013.10.016

Cited by 148 publications

(56 citation statements)

References 15 publications

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“…Figure 8b shows that the NB classifier selected features that perform more consistently throughout all positions. Because NB classifiers are known to be very sensitive to the presence of redundant and/or irrelevant attributes [5], they typically select more consistent features that perform well on average over all positions. For both classifiers, the sensors C and F are the best overall performing sensors.…”

Section: Evaluation Of Individual Orientations and Positionsmentioning

confidence: 99%

Robust Sensor-Orientation-Independent Feature Selection for Animal Activity Recognition on Collar Tags

Kamminga

Meijers

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Fundamental challenges faced by real-time animal activity recognition include variation in motion data due to changing sensor orientations, numerous features, and energy and processing constraints of animal tags. This paper aims at finding small optimal feature sets that are lightweight and robust to the sensor's orientation. Our approach comprises four main steps. First, 3D feature vectors are selected since they are theoretically independent of orientation. Second, the least interesting features are suppressed to speed up computation and increase robustness against overfitting. Third, the features are further selected through an embedded method, which selects features through simultaneous feature selection and classification. Finally, feature sets are optimized through 10-fold cross-validation. We collected real-world data through multiple sensors around the neck of five goats. The results show that activities can be accurately recognized using only accelerometer data and a few lightweight features. Additionally, we show that the performance is robust to sensor orientation and position. A simple Naive Bayes classifier using only a single feature achieved an accuracy of 94 % with our empirical dataset. Moreover, our optimal feature set yielded an average of 94 % accuracy when applied with six other classifiers. This work supports embedded, real-time, energy-efficient, and robust activity recognition for animals.

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Section: Evaluation Of Individual Orientations and Positionsmentioning

confidence: 99%

Robust Sensor-Orientation-Independent Feature Selection for Animal Activity Recognition on Collar Tags

Kamminga

Meijers

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

View full text Add to dashboard Cite

show abstract

“…Many feature selection methods have been proposed, and they are usually classified into three classes: "filter" methods, "wrapper" methods and "embedded" methods [2,7,8,14]. Wu [19] proposed using the fuzzy and grey Delphi methods to identify a set of reliable attributes and, based on these attributes, transforming big data to a manageable scale to consider their impacts.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The concept of "optimal" can be illustrated by two aspects: (1) Elements of the set are highly related to the MCDM problem for decision purpose; (2) The set of attributes is parsimonious, and the selected alternative will be suboptimum if one of these attributes is omitted. The rationale of attribute selection is similar to feature selection in data mining field.…”

Section: Introductionmentioning

confidence: 99%

Attribute Selection Method based on Objective Data and Subjective Preferences in MCDM

Ma¹,

Yi²,

Qu³

et al. 2018

INT J COMPUT COMMUN

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Decision attributes are important parameters when choosing an alternative in a multiple criteria decision-making (MCDM) problem. In order to select the optimal set of decision attributes, an analysis framework is proposed to illustrate the attribute selection problem. Then a two-step attribute selection procedure is presented based on the framework: In the first step, attributes are filtered by using correlation algorithm. In the second step, a multi-objective optimization model is constructed to screen attributes from the results of the first step. Finally, a case study is given to illustrate and verify this method. The advantage of this method is that both external attribute data and subjective decision preferences are utilized in a sequential procedure. It enhances the reliability of decision attributes and matches the actual decision-making scenarios better.

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“…Similar to BN, NB embedded the concept of independence with Bayesian theorem. It employs the concept of conditional probability and successfully implemented for feature subset selection (Bermejo et al 2014). It is also combined with decision tree for multi-class classification task (Farid et al 2014).…”

Section: Related Workmentioning

confidence: 99%

Using machine learning techniques for rising star prediction in co-author network

et al. 2014

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Online bibliographic databases are powerful resources for research in data mining and social network analysis especially co-author networks. Predicting future rising stars is to find brilliant scholars/researchers in co-author networks. In this paper, we propose a solution for rising star prediction by applying machine learning techniques. For classification task, discriminative and generative modeling techniques are considered and two algorithms are chosen for each category. The author, co-authorship and venue based information are incorporated, resulting in eleven features with their mathematical formulations. Extensive experiments are performed to analyze the impact of individual feature, category wise and their combination w.r.t classification accuracy. Then, two ranking lists for top 30 scholars are presented from predicted rising stars. In addition, this concept is demonstrated for prediction of rising stars in database domain. Data from DBLP and Arnetminer databases (1996-2000 for wide disciplines) are used for algorithms' experimental analysis.

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Speeding up incremental wrapper feature subset selection with Naive Bayes classifier

Cited by 148 publications

References 15 publications

Robust Sensor-Orientation-Independent Feature Selection for Animal Activity Recognition on Collar Tags

Robust Sensor-Orientation-Independent Feature Selection for Animal Activity Recognition on Collar Tags

Attribute Selection Method based on Objective Data and Subjective Preferences in MCDM

Using machine learning techniques for rising star prediction in co-author network

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