Dealing with Missing Data: Algorithms Based on Fuzzy Set and Rough Set Theories

Li, Dan; Deogun, Jitender S.; Spaulding, William D.; Shuart, Bill

doi:10.1007/11574798_3

Cited by 14 publications

(4 citation statements)

References 24 publications

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“…Once all descriptors and fingerprints were calculated for each SMILES record in the dataset, preprocessing was performed on the dataset. Proper preprocessing results in clean, correct, and complete data while improper preprocessing may lead to poor results and affect the accuracy and efficiency of the algorithm ( Li et al, 2005a ).…”

Section: Methodsmentioning

confidence: 99%

A deep learning approach to predict blood-brain barrier permeability

Alsenan

Al-Turaiki

Hafez

2021

PeerJ Computer Science

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The blood–brain barrier plays a crucial role in regulating the passage of 98% of the compounds that enter the central nervous system (CNS). Compounds with high permeability must be identified to enable the synthesis of brain medications for the treatment of various brain diseases, such as Parkinson’s, Alzheimer’s, and brain tumors. Throughout the years, several models have been developed to solve this problem and have achieved acceptable accuracy scores in predicting compounds that penetrate the blood–brain barrier. However, predicting compounds with “low” permeability has been a challenging task. In this study, we present a deep learning (DL) classification model to predict blood–brain barrier permeability. The proposed model addresses the fundamental issues presented in former models: high dimensionality, class imbalances, and low specificity scores. We address these issues to enhance the high-dimensional, imbalanced dataset before developing the classification model: the imbalanced dataset is addressed using oversampling techniques and the high dimensionality using a non-linear dimensionality reduction technique known as kernel principal component analysis (KPCA). This technique transforms the high-dimensional dataset into a low-dimensional Euclidean space while retaining invaluable information. For the classification task, we developed an enhanced feed-forward deep learning model and a convolutional neural network model. In terms of specificity scores (i.e., predicting compounds with low permeability), the results obtained by the enhanced feed-forward deep learning model outperformed those obtained by other models in the literature that were developed using the same technique. In addition, the proposed convolutional neural network model surpassed models used in other studies in multiple accuracy measures, including overall accuracy and specificity. The proposed approach solves the problem inevitably faced with obtaining low specificity resulting in high false positive rate.

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Section: Methodsmentioning

confidence: 99%

A deep learning approach to predict blood-brain barrier permeability

Alsenan

Al-Turaiki

Hafez

2021

PeerJ Computer Science

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“…Rough set theory was proposed by Pawlak as a mathematical framework to conceptualize inaccurate, vague, or uncertain data 4,2 . Various applications have been developed in Artificial Intelligence using rough sets, like clustering and classification tools 1,9,17 .…”

Section: Rough Setsmentioning

confidence: 99%

Dealing with Missing Data using a Selection Algorithm on Rough Sets

Prieto-Cubides¹,

Argoty²

2018

IJCIS

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This paper discusses the so-called missing data problem, i.e. the problem of imputing missing values in information systems. A new algorithm, called the ARSI algorithm, is proposed to address the imputation problem of missing values on categorical databases using the framework of rough set theory. This algorithm can be seen as a refinement of the ROUSTIDA algorithm and combines the approach of a generalized non-symmetric similarity relation with a generalized discernibility matrix to predict the missing values on incomplete information systems. Computational experiments show that the proposed algorithm is as efficient and competitive as other imputation algorithms.

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“…An important source of uncertainty to which much attention has not been given in WLA models is uncertainty due to partial ignorance due to missing data or inadequate data. Some previous methods of handling missing data such as deleting cases with incomplete information, or substituting the missing values with estimated mean scores may result in biased data models (Li et al, 2005). Chen and Ma (2007) have quantified the influence of uncertainty of the design flow on WLA by considering it as an uncertain input parameter by computing 95% confidence intervals for the mean values of stream flow.…”

Section: Introductionmentioning

confidence: 99%