A contemporary feature selection and classification framework for imbalanced biomedical datasets

Bikku, Thulasi; Nandam, Sambasiva Rao; Akepogu, Ananda Rao

doi:10.1016/j.eij.2018.03.003

Cited by 26 publications

(14 citation statements)

References 12 publications

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“…We tend to actualize a brimming with life discovering administration with SITINA on Face book to approve our arrangement [19]. The impact of any seed set is characterized dependent on the data dispersion process among the clients the data dissemination is viral promoting, where an organization may wish to spread the reception of another item from some underlying adopters through the social connections between clients [20].…”

Section: Figure 2 Group Of Social Usersmentioning

confidence: 99%

Certified Node Frequency in Social Network Using Parallel Diffusion Methods

Gopi¹,

Narayana²,

Venkatesh³

et al. 2019

ISI

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Informal community examination is one of the key zones of research amid present day times. The informal community is developing with more clients and the ties between them step by step. It has two parts: separating the versatile interpersonal organization into a few networks by considering data dispersion and choosing networks to discover persuasive hubs by a dynamic programming. A dynamic engendering model considering each the overall quality and individual fascination of the talk is given upheld sensible situation for sure, inside and out totally not the same as existing issues with impact decrease. Specifically, every center is distributed a versatility time limit. If the piece time of each customer outperforms that edge, the utility of the framework will decrease. Our proposed technique can discover new networks dependent on the past structure of the system without recomposing them sans preparation. We present an overview of agent techniques managing these issues and propose a scientific categorization that outlines the best in class. The goal is to give a thorough investigation and guide of existing endeavors around data dissemination in informal communities. The proposed calculation utilizes parallel preparing motor to determine this postpone issue in the present situation. The calculation in parallel discovers the prevalent seed set in the system and extends it in parallel to discover the network. Test results on the engineered and true informal organizations show that our strategy is both viable and proficient in finding networks in unique interpersonal organizations.

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Section: Figure 2 Group Of Social Usersmentioning

confidence: 99%

Certified Node Frequency in Social Network Using Parallel Diffusion Methods

Gopi¹,

Narayana²,

Venkatesh³

et al. 2019

ISI

View full text Add to dashboard Cite

show abstract

“…Notwithstanding, a fairly organized picture of clustering approaches can be given. The key fundamental clustering approaches can generally be classified, as shown in Table-1, to the following approaches [17,22]: Table 1-Clustering methods of data in a general perspective [18] Method General Characteristics Partitioning methods -Finds mutually exclusive clusters of spherical shape -Distance based -May use mean or medoid (etc.) to represent cluster center -Effective for small to medium size data sets Hierarchical methods -Clustering is a hierarchical decomposition (i.e., multiple levels) -Cannot correct erroneous merges or splits -May incorporate other techniques like microclustering or consider object "linkages" Density based methods -Can find arbitrarily shaped clusters -Clusters are dense regions of objects in space that are separated by low density regions -Cluster density: Each point must have a minimum number of points within its "neighborhood" -May filter out outliers Grid based methods -Use a multiresolution grid data structure -Fast processing time (typically independent of the number of data objects, yet dependent on grid size) Hierarchical techniques: these techniques integrate data objects into subgroups, which are incorporated into larger and higher-level groups, and the process continues in this manner to formulate a hierarchical tree.…”

Section: Clusteringmentioning

confidence: 99%

Review of medical diagnostics via data mining techniques

Rasheed

faisal

Mohammed

et al. 2021

eijs

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Data mining is one of the most popular analysis methods in medical research. It involves finding patterns and correlations in previously unknown datasets. Data mining encompasses various areas of biomedical research, including data collection, clinical decision support, illness or safety monitoring, public health and inquiry research. Health analytics frequently uses computational methods for data mining, such as clustering, classification, and regression. Studies of large numbers of diverse heterogeneous documents, including biological and electronic information, provided medical and health studies.

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“…It is wholly based on feature similarity of the majority vote of neighbours. A given data point is assigned to that class, which has most common among its k nearest neighbours [16].…”

Section: K-nearest Neighboursmentioning

confidence: 99%

Credit Risk Assessment Using Learning Algorithms for Feature Selection

Hassani

Meybodi

Hajihashemi³

2020

Fuzzy Information and Engineering

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Firefly algorithm is one of the latest outstanding bio-inspired algorithms, which could be manipulated in solving continuous or discrete optimisation problems. In this context, we have utilised the firefly algorithm accompanied by five well-known models of feature selection classifiers to have an accurate estimation of risk, and further to improve the interpret-ability of credit card prediction. One of the significant challenges in the real-world datasets is how to select features. As most of the datasets are unbalanced, the selection of features turns to the maximum class of data that is not fair. To overcome this issue, we have balanced the data using the SMOTE method. Our experimental results on four datasets show that balancing data has increased accuracy. In addition, using a hybrid firefly algorithm, the optimal combination of features that predicts the target class label is achieved. The selected features by the proposed method besides been reduced can represent both majority and minority classes.

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A contemporary feature selection and classification framework for imbalanced biomedical datasets

Cited by 26 publications

References 12 publications

Certified Node Frequency in Social Network Using Parallel Diffusion Methods

Certified Node Frequency in Social Network Using Parallel Diffusion Methods

Review of medical diagnostics via data mining techniques

Credit Risk Assessment Using Learning Algorithms for Feature Selection

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