ILA4: Overcoming missing values in machine learning datasets – An inductive learning approach

Elhassan, Ammar; Abu-Soud, Saleh M.; Alghanim, Firas; Salameh, Walid A.

doi:10.1016/j.jksuci.2021.02.011

Cited by 18 publications

(10 citation statements)

References 17 publications

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“…n=70 participants) from the ADHD group). We determined the 20% cutoff as a compromise solution to preserve a diverse set of features without too strongly negatively impacting the accuracy due to too many missing values 30 . The nal dataset was comprised of 292 participants and 30 features (Table 1).…”

Section: Machine Learning Classi Cationmentioning

confidence: 99%

Training a Machine Learning Classifier to Identify ADHD Based on Real-World Clinical Data From Medical Records

Mikoláš

Vahid

Bernardoni

et al. 2022

Preprint

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The diagnostic process of attention deficit hyperactivity disorder (ADHD) is complex and relies on criteria sensitive to subjective biases. This may cause significant delays in appropriate treatment initiation. An automated analysis relying on subjective and objective measures might not only simplify the diagnostic process and reduce the time to diagnosis, but also improve reproducibility. While recent machine learning studies have succeeded at distinguishing ADHD from healthy controls, the clinical process requires differentiating among other or multiple psychiatric conditions. We trained a linear support vector machine (SVM) classifier to detect participants with ADHD in a population showing a broad spectrum of psychiatric conditions using anonymized data from clinical records (N = 299 participants). We differentiated children and adolescents with ADHD from those not having the condition with an accuracy of 66.1 %. SVM using single features showed slight differences between single features and overlapping standard deviations of the achieved accuracies. An automated feature selection achieved the best performance using a combination 19 features. Real-life clinical data from medical records can be used to automatically identify individuals with ADHD among help-seeking individuals using machine learning. The relevant diagnostic information can be reduced using an automated feature selection without loss of performance. A broad combination of symptoms across different domains, rather than specific domains, seems to indicate an ADHD diagnosis.

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Section: Machine Learning Classi Cationmentioning

confidence: 99%

Training a Machine Learning Classifier to Identify ADHD Based on Real-World Clinical Data From Medical Records

Mikoláš

Vahid

Bernardoni

et al. 2022

Preprint

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“…Problems of missing data values are common in sensor applications. Elhassan and Abu-Soud et al [18] developed an inductive learning algorithm for dealing with the missing data values problem. They focused on enhancing the existing inductive learning algorithm to deal with datasets with missing values and showed a new algorithm that can have the added ability to deal with noise data.…”

Section: Supervised Machine Learningmentioning

confidence: 99%

Advances in Machine Learning for Sensing and Condition Monitoring

Ao¹,

Gelman²,

Karimi³

et al. 2022

Applied Sciences

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In order to overcome the complexities encountered in sensing devices with data collection, transmission, storage and analysis toward condition monitoring, estimation and control system purposes, machine learning algorithms have gained popularity to analyze and interpret big sensory data in modern industry. This paper put forward a comprehensive survey on the advances in the technology of machine learning algorithms and their most recent applications in the sensing and condition monitoring fields. Current case studies of developing tailor-made data mining and deep learning algorithms from practical aspects are carefully selected and discussed. The characteristics and contributions of these algorithms to the sensing and monitoring fields are elaborated.

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“…The data from smart meters is often irregular, with several null and outlying readings mainly due to unstable synchronous transmission between sensors and databases, unexpected device maintenance, storage issues, unreliable/inadequate quality network, the incorrect estimate of sent data, and various unknown environmental factors [26]. Such irregularities in the dataset may jeopardize the learning ability of the SML classifier, resulting in biased and erroneous estimations [27]. In order to address this issue, typically, two approaches have been adopted in literature: imputation or elimination.…”

Section: Literature Reviewmentioning

confidence: 99%

Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel‐Tree boosting classifier—A novel sequentially executed supervised machine learning approach

Hussain

Mustafa

Al-Shqeerat

et al. 2022

IET Generation Trans & Dist

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This paper presents a novel, sequentially executed supervised machine learning-based electric theft detection framework using a Jaya-optimized combined Kernel and Tree Boosting (KTBoost) classifier. It utilizes the intelligence of the XGBoost algorithm to estimate the missing values in the acquired dataset during the data pre-processing phase. An oversampling algorithm based on the Robust-SMOTE technique is utilized to avoid the unbalanced data class distribution issue. Afterward, with the aid of few very significant statistical, temporal, and spectral features extracted from the acquired kWh dataset, the complex underlying data patterns are comprehended to enhance the accuracy and detection rate of the classifier. For effectively classifying the consumers into "Honest" and "Fraudster," the ensemble machine learning-based classifier KTBoost, with Jaya algorithm optimized hyperparameters, is utilized. Finally, the developed model is re-trained using a reduced set of highly important features to minimize the computational resources without compromising the performance of the developed model. The outcome of this study reveals that the proposed theft detection method achieves the highest accuracy (93.38%), precision (95%), and recall (93.18%) among all the studied methods, thus signifying its importance in the studied area of research.

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ILA4: Overcoming missing values in machine learning datasets – An inductive learning approach

Cited by 18 publications

References 17 publications

Training a Machine Learning Classifier to Identify ADHD Based on Real-World Clinical Data From Medical Records

Training a Machine Learning Classifier to Identify ADHD Based on Real-World Clinical Data From Medical Records

Advances in Machine Learning for Sensing and Condition Monitoring

Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel‐Tree boosting classifier—A novel sequentially executed supervised machine learning approach

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