Optimization Of Neural Network Inputs By Feature Selection Methods

2011 ◽  
Author(s):  
Michal Prochazka ◽  
Zuzana Oplatkova ◽  
Jiri Holoska ◽  
Vladimir Gerlich
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Selection Methods

scholarly journals Multi-Layer Perceptron Neural Network Model Development for Chili Pepper Disease Diagnosis Using Filter and Wrapper Feature Selection Methods

2021 ◽  
Vol 11 (5) ◽  
pp. 7714-7719
Author(s):  
S. Nuanmeesri ◽  
W. Sriurai
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Information Gain ◽  
Disease Diagnosis ◽  
Chili Pepper ◽  
Growth Stages ◽  
Multi Layer Perceptron ◽  
Selection Methods ◽  
Diagnosis Model ◽  
Wrapper Feature Selection

The goal of the current study is to develop a diagnosis model for chili pepper disease diagnosis by applying filter and wrapper feature selection methods as well as a Multi-Layer Perceptron Neural Network (MLPNN). The data used for developing the model include 1) types, 2) causative agents, 3) areas of infection, 4) growth stages of infection, 5) conditions, 6) symptoms, and 7) 14 types of chili pepper diseases. These datasets were applied to the 3 feature selection techniques, including information gain, gain ratio, and wrapper. After selecting the key features, the selected datasets were utilized to develop the diagnosis model towards the application of MLPNN. According to the model’s effectiveness evaluation results, estimated by 10-fold cross-validation, it can be seen that the diagnosis model developed by applying the wrapper method along with MLPNN provided the highest level of effectiveness, with an accuracy of 98.91%, precision of 98.92%, and recall of 98.89%. The findings showed that the developed model is applicable.

scholarly journals Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection

2017 ◽  
Vol 231 (11) ◽  
pp. 1048-1063 ◽  
Author(s):  
Omneya Attallah ◽  
Alan Karthikesalingam ◽  
Peter JE Holt ◽  
Matthew M Thompson ◽  
Rob Sayers ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Selection ◽  
Survival Data ◽  
Majority Voting ◽  
Selection Methods ◽  
Multiple Classifier System ◽  
Machine Learning Classifiers ◽  
Classifier System ◽  
Multiple Classifier

Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox’s proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox’s model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients’ future follow-up plan.

scholarly journals Stability of feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation

2021 ◽  
Author(s):  
Hryhorii Chereda ◽  
Andreas Leha ◽  
Tim Beissbarth
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Feature Selection ◽  
Deep Learning ◽  
Patient Specific ◽  
Molecular Signatures ◽  
Expression Data ◽  
Selection Methods ◽  
Feature Sets ◽  
The Stability

Motivation: High-throughput technologies play a more and more significant role in discovering prognostic molecular signatures and identifying novel drug targets. It is common to apply Machine Learning (ML) methods to classify high-dimensional gene expression data and to determine a subset of features (genes) that is important for decisions of a ML model. One feature subset of important genes corresponds to one dataset and it is essential to sustain the stability of feature sets across different datasets with the same clinical endpoint since the selected genes are candidates for prognostic biomarkers. The stability of feature selection can be improved by including information of molecular networks into ML methods. Gene expression data can be assigned to the vertices of a molecular network's graph and then classified by a Graph Convolutional Neural Network (GCNN). GCNN is a contemporary deep learning approach that can be applied to graph-structured data. Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. In our recent work we developed Graph Layer-wise Relevance Propagation (GLRP) --- a method that adapts LRP to a graph convolution and explains patient-specific decisions of GCNN. GLRP delivers individual molecular signatures as patient-specific subnetworks that are parts of a molecular network representing background knowledge about biological mechanisms. GLRP gives a possibility to deliver the subset of features corresponding to a dataset as well, so that the stability of feature selection performed by GLRP can be measured and compared to that of other methods. Results: Utilizing two large breast cancer datasets, we analysed properties of feature sets selected by GLRP (GCNN+LRP) such as stability and permutation importance. We have implemented a graph convolutional layer of GCNN as a Keras layer so that the SHAP (SHapley Additive exPlanation) explanation method could be also applied to a Keras version of a GCNN model. We compare the stability of feature selection performed by GCNN+LRP to the stability of GCNN+SHAP and to other ML based feature selection methods. We conclude, that GCNN+LRP shows the highest stability among other feature selection methods including GCNN+SHAP. It was established that the permutation importance of features among GLRP subnetworks is lower than among GCNN+SHAP subnetworks, but in the context of the utilized molecular network, a GLRP subnetwork of an individual patient is on average substantially more connected (and interpretable) than a GCNN+SHAP subnetwork, which consists mainly of single vertices.

scholarly journals Forecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259575
Author(s):  
Wenbin Bi ◽  
Qiusheng Zhang
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Mergers And Acquisitions ◽  
Information Gain ◽  
Capital Expenditure ◽  
Feature Selection Method ◽  
Activation Function ◽  
Gradient Boosting ◽  
Selection Methods ◽  
Chi2 Test

Traditional forecasting methods in mergers and acquisitions (M&A) data have two limitations that significantly reduce forecasting accuracy: (1) the imbalance of data, that is, the failure cases of M&A are far fewer than the successful cases (82%/18% of our sample), and (2) both the bidder and the target of the merger have numerous descriptive features, making it difficult to choose which ones to forecast. This study proposes a neural network using partial-sigmoid (i.e., partial-sigmoid neural network [PSNN]) as the activation function of the output layer and compares three feature selection methods, namely, chi-square (chi2) test, information gain and gradient boosting decision tree (GBDT). Experimental results prove that our PSNN (improved up to 0.37 precision, 0.49 recall, 0.41 G-Mean and 0.23 F1-measure) and feature selection (improved 1.83%-13.16% accuracy) method can effectively improve the adverse effects of the defects of the above two merger data on forecasting. Scholars who studied the forecast of merger failure have overlooked three important features: assets of the previous year, market value and capital expenditure. The chi2 test feature selection method is the best among the three feature selection methods.

scholarly journals Deep Neural Network Feature Selection Approaches for Data-Driven Prognostic Model of Aircraft Engines

Aerospace ◽  
2020 ◽  
Vol 7 (9) ◽  
pp. 132
Author(s):  
Phattara Khumprom ◽  
David Grewell ◽  
Nita Yodo
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Gas Turbine ◽  
Deep Neural Network ◽  
Data Driven ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Aircraft Engines ◽  
Selection Methods ◽  
Wrapper Feature Selection

Predicting Remaining Useful Life (RUL) of systems has played an important role in various fields of reliability engineering analysis, including in aircraft engines. RUL prediction is critically an important part of Prognostics and Health Management (PHM), which is the reliability science that is aimed at increasing the reliability of the system and, in turn, reducing the maintenance cost. The majority of the PHM models proposed during the past few years have shown a significant increase in the amount of data-driven deployments. While more complex data-driven models are often associated with higher accuracy, there is a corresponding need to reduce model complexity. One possible way to reduce the complexity of the model is to use the features (attributes or variables) selection and dimensionality reduction methods prior to the model training process. In this work, the effectiveness of multiple filter and wrapper feature selection methods (correlation analysis, relief forward/backward selection, and others), along with Principal Component Analysis (PCA) as a dimensionality reduction method, was investigated. A basis algorithm of deep learning, Feedforward Artificial Neural Network (FFNN), was used as a benchmark modeling algorithm. All those approaches can also be applied to the prognostics of an aircraft gas turbine engines. In this paper, the aircraft gas turbine engines data from NASA Ames prognostics data repository was used to test the effectiveness of the filter and wrapper feature selection methods not only for the vanilla FFNN model but also for Deep Neural Network (DNN) model. The findings show that applying feature selection methods helps to improve overall model accuracy and significantly reduced the complexity of the models.

Evaluation of feature selection methods based on artificial neural network weights

2021 ◽  
Vol 168 ◽  
pp. 114312
Author(s):  
Nattane Luíza da Costa ◽  
Márcio Dias de Lima ◽  
Rommel Barbosa
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Selection ◽  
Selection Methods ◽  
Artificial Neural
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