Bootstrapping and multiple imputation ensemble approaches for classification problems

Khan, Shehroz S.; Ahmad, Amir; Mihailidis, Alex

doi:10.3233/jifs-182656

Cited by 19 publications

(12 citation statements)

References 32 publications

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“…MV and ICD continue to be prevalent in numerous real-world problems and across many application areas [6], [7], including water quality anomaly detection domain. Consequently, these occurrences have continued to generate lots of attention from researchers because the majority of conventional predictive machine learning algorithms are not developed to handle these challenge in data, because they assume completeness of data and a balanced class distribution [8], [9]. As a result, predictive or classification algorithms perform sub-optimally on these kinds of datasets if not properly handled, resulting in bias, inaccurate and lowquality predictive performance of the classifiers [7], [8], [10].…”

Section: Introductionmentioning

confidence: 99%

Empirical Comparison of Approaches for Mitigating Effects of Class Imbalances in Water Quality Anomaly Detection

et al. 2020

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Imbalanced class distribution and missing data are two common problems and occurrences in water quality anomaly detection domain. Learning algorithms in an imbalanced dataset can yield an overrated classification accuracy driven by a bias towards the majority class at the expense of the minority class. On the other hand, missing values in data can induce complexity in the learning classifiers during data analysis. These two problems pose substantial challenges to the performance of learning algorithms in real-life water quality anomaly detection problems. Hence, the need for them to be carefully considered and addressed to achieve better performance. In this paper, the performance of a range of several combinations of techniques to deal with imbalanced classes in the context of binary-imbalanced water quality anomaly detection problem and the presence of missing values is extensively compare. The methods considered include seven missing data and eight resampling methods, on ten different learning state-of-the-art classifiers taking into account diversity in their learning philosophies. The different classifiers are evaluated using stratified 5-fold cross-validation, based on three performance evaluation metrics namely accuracy, ROC-AUC and F1-measure. Further experiments are carried out on seventeen variants of homogenous and heterogeneous ensemble techniques embedded with resampling and missing value strategies during their training phase as well as two optimized deep neural network models. The experimental results show an improvement in the performance of the learning classifiers, especially when dealing with the class imbalance problem (on the one hand) and the incomplete data problem (on the other hand). Furthermore, neural network models exhibit superior performance when dealing with both problems.

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Section: Introductionmentioning

confidence: 99%

Empirical Comparison of Approaches for Mitigating Effects of Class Imbalances in Water Quality Anomaly Detection

et al. 2020

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“…Nevertheless, there is still a long way until such systems will reach industry-ready stages, both in terms of practical accuracy and affordability. While current research shows promising results predominantly in cattle and pigs, there are still many avenues to be explored for better automation of the whole BW estimation process, such as 1) the ability of CV and ML/DL hybrid BW predictive systems to cope with missing information possibly via imputation and data enhancement approaches as suggested by ( Khan et al, 2019 ), 2) the generalization power and the breed-agnostic prediction for any given species, 3) the automatic detection and identification of an animal remotely, and 4) the automatic recognition and adaptability based on an animal’s development stage, posture, and position within a sensor’s field of view.…”

Section: Discussionmentioning

confidence: 99%

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

Wang

Shadpour

Chan

et al. 2021

Journal of Animal Science

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Monitoring, recording, and predicting livestock body weight (BW) allows for timely intervention in diets and health, greater efficiency in genetic selection, and identification of optimal times to market animals because animals that have already reached the point of slaughter represent a burden for the feedlot. There are currently two main approaches (direct and indirect) to measure the BW in livestock. Direct approaches include partial-weight or full-weight industrial scales placed in designated locations on large farms that measure passively or dynamically the weight of livestock. While these devices are very accurate, their acquisition, intended purpose and operation size, repeated calibration and maintenance costs associated with their placement in high-temperature variability, and corrosive environments are significant and beyond the affordability and sustainability limits of small and medium size farms and even of commercial operators. As a more affordable alternative to direct weighing approaches, indirect approaches have been developed based on observed or inferred relationships between biometric and morphometric measurements of livestock and their BW. Initial indirect approaches involved manual measurements of animals using measuring tapes and tubes and the use of regression equations able to correlate such measurements with BW. While such approaches have good BW prediction accuracies, they are time consuming, require trained and skilled farm laborers, and can be stressful for both animals and handlers especially when repeated daily. With the concomitant advancement of contactless electro-optical sensors (e.g., 2D, 3D, infrared cameras), computer vision (CV) technologies, and artificial intelligence fields such as machine learning (ML) and deep learning (DL), 2D and 3D images have started to be used as biometric and morphometric proxies for BW estimations. This manuscript provides a review of CV-based and ML/DL-based BW prediction methods and discusses their strengths, weaknesses, and industry applicability potential.

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“…Approaches in literature on missing values handling using ensemble methods are discussed in the following. Authors in [122], proposed a bootstrapping ensemble to model uncertainty and variety in the data that has high missingness. They performed an extensive evaluation of their approach by varying the missingness ratio of the missing data.…”

Section: Ensemble Methodsmentioning

confidence: 99%

A Survey On Missing Data in Machine Learning

Emmanuel

Maupong

Mpoeleng

et al. 2021

Preprint

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Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur as a result of various factors like missing completely at random, missing at random or missing not at random. All these may be as a result of system malfunction during data collection or human error during data pre-processing. Nevertheless, it is important to deal with missing values before analysing data since ignoring or omitting missing values may result in biased or misinformed analysis. In literature there have been several proposals for handling missing values. In this paper we aggregate some of the literature on missing data particularly focusing on machine learning techniques. We also give insight on how the machine learning approaches work by highlighting the key features of the proposed techniques, how they perform, their limitations and the kind of data they are most suitable for. Finally, we experiment on the K nearest neighbor and random forest imputation techniques on novel power plant induced fan data and offer some possible future research direction.

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Bootstrapping and multiple imputation ensemble approaches for classification problems

Cited by 19 publications

References 32 publications

Empirical Comparison of Approaches for Mitigating Effects of Class Imbalances in Water Quality Anomaly Detection

Empirical Comparison of Approaches for Mitigating Effects of Class Imbalances in Water Quality Anomaly Detection

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

A Survey On Missing Data in Machine Learning

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