scholarly journals Graph Based Feature Selection for Reduction of Dimensionality in Next-Generation RNA Sequencing Datasets

Algorithms ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 21
Author(s):  
Consolata Gakii ◽  
Paul O. Mireji ◽  
Richard Rimiru
Keyword(s):  
Feature Selection ◽  
Association Rule ◽  
Association Rule Mining ◽  
Principal Component ◽  
Recursive Feature Elimination ◽  
High Dimensional ◽  
Rule Mining ◽  
Rnaseq Data ◽  
Feature Selection Approach ◽  
Feature Selection Techniques

Analysis of high-dimensional data, with more features () than observations () (), places significant demand in cost and memory computational usage attributes. Feature selection can be used to reduce the dimensionality of the data. We used a graph-based approach, principal component analysis (PCA) and recursive feature elimination to select features for classification from RNAseq datasets from two lung cancer datasets. The selected features were discretized for association rule mining where support and lift were used to generate informative rules. Our results show that the graph-based feature selection improved the performance of sequential minimal optimization (SMO) and multilayer perceptron classifiers (MLP) in both datasets. In association rule mining, features selected using the graph-based approach outperformed the other two feature-selection techniques at a support of 0.5 and lift of 2. The non-redundant rules reflect the inherent relationships between features. Biological features are usually related to functions in living systems, a relationship that cannot be deduced by feature selection and classification alone. Therefore, the graph-based feature-selection approach combined with rule mining is a suitable way of selecting and finding associations between features in high-dimensional RNAseq data.

scholarly journals Association rule mining algorithms on high-dimensional datasets

2018 ◽  
Vol 23 (3) ◽  
pp. 420-427 ◽  
Author(s):  
Dongmei Ai ◽  
Hongfei Pan ◽  
Xiaoxin Li ◽  
Yingxin Gao ◽  
Di He
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
High Dimensional ◽  
Rule Mining ◽  
Mining Algorithms ◽  
High Dimensional Datasets

The Use of Association-Rule Mining and High-Dimensional Visualization To Explore the Impact of Geological Features on Dynamic-Flow Behavior

SPE Journal ◽  
2016 ◽  
Vol 21 (06) ◽  
pp. 1996-2009 ◽  
Author(s):  
Satomi Suzuki ◽  
Dave Stern ◽  
Tom Manzocchi
Keyword(s):  
Reservoir Simulation ◽  
Association Rule ◽  
Association Rule Mining ◽  
High Dimensional ◽  
Rule Mining ◽  
Geological Uncertainty ◽  
Flow Response ◽  
Geological Features ◽  
Simulation Results ◽  
The Impact

Summary Because of computational advances in reservoir simulation with high-performance computing, it is now possible to simulate more than thousands of reservoir-simulation cases in a practical time frame. This opens a new avenue to reservoir-simulation studies, enabling exhaustive exploration of subsurface uncertainty and development/depletion options. However, analyzing the results of a large number of simulation cases still remains a challenging and overwhelming task. We propose a new method that enables the efficient analysis of massive reservoir-simulation results, often consisting of thousands of cases, by discovering interesting patterns of relationships among variables in large data sets. The method uses a well-known data-mining method, called association-rule mining, together with a high-dimensional visualization technique. We demonstrate the capability of the proposed method by using it to analyze the reservoir-simulation results from the Sensitivity Analysis of the Impact of Geological Uncertainty on Production (SAIGUP) project, which is an interdisciplinary reservoir-modeling project carried out earlier by Manzocchi et al. (2008a). To investigate the influence of geological features on oil recovery in shallow marine reservoirs, numerous reservoir models were built and flow-simulated in the SAIGUP project. In this paper, we analyze the simulation results from an ensemble of 9,072 models, which cover all possible combinations of several structural and sedimentological parameters individually varied to describe geological uncertainty. To be able to analyze the simulation results from such exhaustive sampling from high-dimensional model parameter space, it is crucial to efficiently decompose complex interactions between model parameters and to discover hidden impacts on flow response. By coupling the association-rule mining algorithm and high-dimensional visualization, such interactions and impacts are rapidly extracted and visualized in such a way that engineers and geoscientists can interpret meaningful sensitivities “at a glance.” This methodology provides a novel way to rapidly interpret flow response from a large ensemble of reservoir models without being overwhelmed by massive data.

Reducing the Feature Space Using Constraint-Governed Association Rule Mining

2017 ◽  
Vol 26 (1) ◽  
pp. 139-152
Author(s):  
◽  
M. Umme Salma
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Association Rule ◽  
Association Rule Mining ◽  
Medical Data ◽  
Cancer Surveillance ◽  
Feature Subset ◽  
Rule Mining ◽  
Data Set

AbstractRecent advancements in science and technology and advances in the medical field have paved the way for the accumulation of huge amount of medical data in the digital repositories, where they are stored for future endeavors. Mining medical data is the most challenging task as the data are subjected to many social concerns and ethical issues. Moreover, medical data are more illegible as they contain many missing and misleading values and may sometimes be faulty. Thus, pre-processing tasks in medical data mining are of great importance, and the main focus is on feature selection, because the quality of the input determines the quality of the resultant data mining process. This paper provides insight to develop a feature selection process, where a data set subjected to constraint-governed association rule mining and interestingness measures results in a small feature subset capable of producing better classification results. From the results of the experimental study, the feature subset was reduced to more than 50% by applying syntax-governed constraints and dimensionality-governed constraints, and this resulted in a high-quality result. This approach yielded about 98% of classification accuracy for the Breast Cancer Surveillance Consortium (BCSC) data set.

scholarly journals Association Rule-Based Feature Mining for Automated Fault Diagnosis of Rolling Bearing

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yuan Li ◽  
Jinjiang Wang ◽  
Lixiang Duan ◽  
Tangbo Bai ◽  
Xuduo Wang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Matrix ◽  
Fault Classification ◽  
Rule Mining ◽  
Bearing Fault ◽  
New Association

Effective and efficient diagnosis methods are highly demanded to improve system reliability. Comparing with conventional fault diagnosis methods taking a forward approach (e.g., feature extraction, feature selection, and fusion, and then fault diagnosis), this paper presents a new association rule mining method which provides an inverse approach unearthing the underlying relation between labeled defects and extracted features for bearing fault analysis. Instead of evenly dividing methods used in traditional association rule mining, a new association rule mining approach based on the equal probability discretization method is presented in this study. First, a series of extracted features of signal data are discretized following the guideline of equalized probability distribution of the data in order to avoid excessive concentration or decentralized data. Next, the data matrix composed of arrays of discretized features and defect labels is exploited to generate the association rules representing the relation between the features and fault types. Experimental study on a bearing test reveals that the proposed method can generate a series of underlying association rules for bearing fault diagnosis, and the related features selected by the proposed method can be used directly to analyze bearing signals for fault classification and defect severity identification. As a new feature selection method, it possesses prominent superiority compared to traditional PCA, KPCA, and LLE dimension reduction methods.

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