Scalable feature selection using ReliefF aided by locality‐sensitive hashing

Eiras-Franco, Carlos; Guijarro-Berdiñas, Bertha; Alonso‐Betanzos, Amparo; Bahamonde, Antonio

doi:10.1002/int.22546

Cited by 16 publications

(3 citation statements)

References 31 publications

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“…ReliefF is not a metric, but an algorithm [ 9 ]. The ReliefF algorithm estimates the quality of features based on how well the feature can differentiate between similar instances.…”

Section: Materials and Methodsmentioning

confidence: 99%

Multi-Class Classification of Medical Data Based on Neural Network Pruning and Information-Entropy Measures

Sánchez-Gutiérrez

González-Pérez

2022

Entropy

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Medical data includes clinical trials and clinical data such as patient-generated health data, laboratory results, medical imaging, and different signals coming from continuous health monitoring. Some commonly used data analysis techniques are text mining, big data analytics, and data mining. These techniques can be used for classification, clustering, and machine learning tasks. Machine learning could be described as an automatic learning process derived from concepts and knowledge without deliberate system coding. However, finding a suitable machine learning architecture for a specific task is still an open problem. In this work, we propose a machine learning model for the multi-class classification of medical data. This model is comprised of two components—a restricted Boltzmann machine and a classifier system. It uses a discriminant pruning method to select the most salient neurons in the hidden layer of the neural network, which implicitly leads to a selection of features for the input patterns that feed the classifier system. This study aims to investigate whether information-entropy measures may provide evidence for guiding discriminative pruning in a neural network for medical data processing, particularly cancer research, by using three cancer databases: Breast Cancer, Cervical Cancer, and Primary Tumour. Our proposal aimed to investigate the post-training neuronal pruning methodology using dissimilarity measures inspired by the information-entropy theory; the results obtained after pruning the neural network were favourable. Specifically, for the Breast Cancer dataset, the reported results indicate a 10.68% error rate, while our error rates range from 10% to 15%; for the Cervical Cancer dataset, the reported best error rate is 31%, while our proposal error rates are in the range of 4% to 6%; lastly, for the Primary Tumour dataset, the reported error rate is 20.35%, and our best error rate is 31%.

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“…ReliefF is not a metric, but an algorithm [ 9 ]. The ReliefF algorithm estimates the quality of features based on how well the feature can differentiate between similar instances.…”

Section: Materials and Methodsmentioning

confidence: 99%

Multi-Class Classification of Medical Data Based on Neural Network Pruning and Information-Entropy Measures

Sánchez-Gutiérrez

González-Pérez

2022

Entropy

View full text Add to dashboard Cite

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“…In general, the feature selection algorithms are classified into three: embedded scheme, filter‐based scheme as well as wrapper scheme. In this article, a modified relief technique is employed to select optimal features thereby performing better classification 39 . The modified relief technique is highly robust and can deal with both real time and noisy data.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…In this article, a modified relief technique is employed to select optimal features thereby performing better classification. 39 The modified relief technique is highly robust and can deal with both real time and noisy data. At first, in modified relief algorithm, random selection of instances takes place for the k-nearest neighbor.…”

Section: Feature Selection Process Using Modified Relief Algorithmmentioning

confidence: 99%

A novel deep neural network based marine predator model for effective classification of big data from social Internet of Things

Shaji

Singh

Nisha

2022

Concurrency and Computation

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Social Internet of Things (SIoT) is considered as one of the most recently evolved topics that connects people and object, object and object as well as people and people. The SIoT and big data provide an exact representation of IoT and social system for human progression characterization. Numerous machine learning techniques are employed to classify the data gathered from SIoT in a more powerful way. In this article, a deep neural network based marine predator (DRNN-MP) algorithm is proposed in classifying big data. Here, an adaptive Savitzky-Golay filter is employed for selecting the subset and to eliminate undesirable data, as well as different noises. The big data databases are reduced using a Hadoop map reducing framework thereby enhancing the performances of the proposed approach. In addition to this, a modified relief technique is employed to select optimal features thereby performing better classification. The testing and training process based on the proposed approach for optimal classification of features from the big data considers four different databases namely coronary illness, GPS trajectories, localization data, and water treatment plant obtained from UCI machine learning repository. In addition to this, the proposed approach is evaluated for diverse performance measures namely accuracy, precision, specificity, sensitivity, throughput, and energy consumption. Finally, the proposed approach is compared for various metrics to illustrate the effectiveness of the system and the results demonstrated that the accuracy of our work is 98.25%. K E Y W O R D Sbig data, database, deep recurrent neural network, marine predator, modified relief, social internet of things INTRODUCTIONInternet of things (IoT) illustrates a separate universe of heterogeneous objects namely actuators, sensors as well as smartphones in which each object and everything possesses a sovereign identity. 1 Nowadays, developing an IoT enabled technology and providing solutions are considered as tedious tasks. On the other hand, IoT deals with sharing and pervasive collection of data regarding a common intention. In IoT, various attribute factors namely integer value and variable define the data thereby permitting specific functions to be performed using a predetermined interface. 2Numerous research scholars are fascinated peculiarly in determining the risk problems emerging while integrating and discovering the data withinIoT. In recent years, the social Internet of Things (SIoT) has been established which is another utilization of IoT. 3 They constitute various distinctive characteristics including diverse communication protocols, operating systems, related standards, and various operational platforms; however, all such differences are neglected. 4

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A New Feature Selection Algorithm Based on Adversarial Learning for Solving Classification Problems

Jin,

Wei,

Zha

et al. 2024

Intelligence Computation and Applications

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Scalable feature selection using ReliefF aided by locality‐sensitive hashing

Cited by 16 publications

References 31 publications

Multi-Class Classification of Medical Data Based on Neural Network Pruning and Information-Entropy Measures

Multi-Class Classification of Medical Data Based on Neural Network Pruning and Information-Entropy Measures

A novel deep neural network based marine predator model for effective classification of big data from social Internet of Things

A New Feature Selection Algorithm Based on Adversarial Learning for Solving Classification Problems

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