Group feature selection for enhancing information gain in MRI reconstruction

2021 ◽  
Author(s):  
Yuchou Chang ◽  
Mert Saritac
Keyword(s):  
Feature Selection ◽  
Information Gain ◽  
Learning Performance ◽  
Kernel Learning ◽  
Reconstructed Image ◽  
Selection Strategy ◽  
Parallel Mri ◽  
Mri Reconstruction ◽  
Space Data ◽  
Imaging Speed

Abstract Magnetic resonance imaging (MRI) has revolutionized the radiology. As a leading medical imaging modality, MRI not only visualizes the structures inside body, but also produces functional imaging. However, due to the slow imaging speed constrained by the MR physics, MRI cost is expensive, and patient may feel not comfortable in a scanner for a long time. Parallel MRI has accelerated the imaging speed through the sub-Nyquist sampling strategy and the missing data are interpolated by the multiple coil data acquired. Kernel learning has been used in the parallel MRI reconstruction to learn the interpolation weights and re-construct the undersampled data. However, noise and aliasing artifacts still exist in the reconstructed image and a large number of auto-calibration signal lines are needed. To further improve the kernel learning-based MRI reconstruction and accelerate the speed, this paper proposes a group feature selection strategy to improve the learning performance and enhance the reconstruction quality. An explicit kernel mapping is used for selecting a subset of features which contribute most to estimate the missing k-space data. The experimental results show that the learning behaviours can be better predicted and therefore the reconstructed image quality is improved.

Feature Selection Algorithm for High-dimensional Biomedical Data Using Information Gain and Improved Chemical Reaction Optimization

2021 ◽  
Vol 15 (8) ◽  
pp. 912-926
Author(s):  
Ge Zhang ◽  
Pan Yu ◽  
Jianlin Wang ◽  
Chaokun Yan
Keyword(s):  
Feature Selection ◽  
Chemical Reaction ◽  
Information Gain ◽  
Feature Selection Method ◽  
Search Space ◽  
Neighborhood Search ◽  
Biomedical Data ◽  
Chemical Reaction Optimization ◽  
Search Mechanism ◽  
Reaction Optimization

Background: There have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. However, these datasets usually involve thousands of features and include much irrelevant or redundant information, which leads to confusion during diagnosis. Feature selection is a solution that consists of finding the optimal subset, which is known to be an NP problem because of the large search space. Objective: For the issue, this paper proposes a hybrid feature selection method based on an improved chemical reaction optimization algorithm (ICRO) and an information gain (IG) approach, which called IGICRO. Methods: IG is adopted to obtain some important features. The neighborhood search mechanism is combined with ICRO to increase the diversity of the population and improve the capacity of local search. Results: Experimental results of eight public available data sets demonstrate that our proposed approach outperforms original CRO and other state-of-the-art approaches.

Analysis of Feature Selection and Ensemble Classifier Methods for Intrusion Detection

2018 ◽  
Vol 7 (1) ◽  
pp. 57-72
Author(s):  
H.P. Vinutha ◽  
Poornima Basavaraju
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Detection Rate ◽  
Information Gain ◽  
False Positive Rate ◽  
Ensemble Classifier ◽  
Ensemble Classification ◽  
Chi Square ◽  
Traffic Pattern ◽  
Data Mining Algorithms

Day by day network security is becoming more challenging task. Intrusion detection systems (IDSs) are one of the methods used to monitor the network activities. Data mining algorithms play a major role in the field of IDS. NSL-KDD'99 dataset is used to study the network traffic pattern which helps us to identify possible attacks takes place on the network. The dataset contains 41 attributes and one class attribute categorized as normal, DoS, Probe, R2L and U2R. In proposed methodology, it is necessary to reduce the false positive rate and improve the detection rate by reducing the dimensionality of the dataset, use of all 41 attributes in detection technology is not good practices. Four different feature selection methods like Chi-Square, SU, Gain Ratio and Information Gain feature are used to evaluate the attributes and unimportant features are removed to reduce the dimension of the data. Ensemble classification techniques like Boosting, Bagging, Stacking and Voting are used to observe the detection rate separately with three base algorithms called Decision stump, J48 and Random forest.

Support Vector Machine VS Information Gain: Analisis Sentimen Cyberbullying di Twitter Indonesia

2020 ◽  
Vol 11 (2) ◽  
pp. 107-111
Author(s):  
Christevan Destitus ◽  
Wella Wella ◽  
Suryasari Suryasari
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Text Mining ◽  
Information Gain ◽  
Text Processing ◽  
Support Vector ◽  
Term Weighting ◽  
System Process ◽  
Research Stage

This study aims to clarify tweets on twitter using the Support Vector Machine and Information Gain methods. The clarification itself aims to find a hyperplane that separates the negative and positive classes. In the research stage, there is a system process, namely text mining, text processing which has stages of tokenizing, filtering, stemming, and term weighting. After that, a feature selection is made by information gain which calculates the entropy value of each word. After that, clarify based on the features that have been selected and the output is in the form of identifying whether the tweet is bully or not. The results of this study found that the Support Vector Machine and Information Gain methods have sufficiently maximum results.

scholarly journals A Robust Gene selection Method for Microarray-based Cancer Classification

2010 ◽  
Vol 9 ◽  
pp. CIN.S3794 ◽  
Author(s):  
Xiaosheng Wang ◽  
Osamu Gotoh
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Information Gain ◽  
Expression Profiles ◽  
Feature Selection Method ◽  
Gene Expression Profiles ◽  
Molecular Classification ◽  
Selection Method ◽  
Chi Square

Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection approach proposed in our previous work, which was the generalization of the feature selection method based on the depended degree of attribute in rough sets. We compared the feature selection method with the established methods: the depended degree, chi-square, information gain, Relief-F and symmetric uncertainty, and analyzed its properties through a series of classification experiments. The results revealed that our method was superior to the canonical depended degree of attribute based method in robustness and applicability. Moreover, the method was comparable to the other four commonly used methods. More importantly, the method can exhibit the inherent classification difficulty with respect to different gene expression datasets, indicating the inherent biology of specific cancers.

scholarly journals Feature Selection using Genetic Programming

2019 ◽  
Vol 3 (2) ◽  
pp. 11-18
Author(s):  
George Mweshi
Keyword(s):  
Feature Selection ◽  
Genetic Programming ◽  
Information Gain ◽  
Principal Component ◽  
Search Space ◽  
Classification Performance ◽  
The Other ◽  
Searching Strategy ◽  
Mining Algorithms ◽  
Feature Selection Techniques

Extracting useful and novel information from the large amount of collected data has become a necessity for corporations wishing to maintain a competitive advantage. One of the biggest issues in handling these significantly large datasets is the curse of dimensionality. As the dimension of the data increases, the performance of the data mining algorithms employed to mine the data deteriorates. This deterioration is mainly caused by the large search space created as a result of having irrelevant, noisy and redundant features in the data. Feature selection is one of the various techniques that can be used to remove these unnecessary features. Feature selection consequently reduces the dimension of the data as well as the search space which in turn increases the efficiency and the accuracy of the mining algorithms. In this paper, we investigate the ability of Genetic Programming (GP), an evolutionary algorithm searching strategy capable of automatically finding solutions in complex and large search spaces, to perform feature selection. We implement a basic GP algorithm and perform feature selection on 5 benchmark classification datasets from UCI repository. To test the competitiveness and feasibility of the GP approach, we examine the classification performance of four classifiers namely J48, Naives Bayes, PART, and Random Forests using the GP selected features, all the original features and the features selected by the other commonly used feature selection techniques i.e. principal component analysis, information gain, relief-f and cfs. The experimental results show that not only does GP select a smaller set of features from the original features, classifiers using GP selected features achieve a better classification performance than using all the original features. Furthermore, compared to the other well-known feature selection techniques, GP achieves very competitive results.

scholarly journals Extracting salient features for network intrusion detection using machine learning methods

2014 ◽  
Vol 52 ◽  
Author(s):  
Ralf C. Staudemeyer ◽  
Christian W. Omlin
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Information Gain ◽  
Selection Process ◽  
Denial Of Service ◽  
Reduction Process ◽  
Feature Reduction ◽  
General Nature ◽  
Network Intrusion ◽  
Salient Features

This work presents a data preprocessing and feature selection framework to support data mining and network security experts in minimal feature set selection of intrusion detection data. This process is supported by detailed visualisation and examination of class distributions. Distribution histograms, scatter plots and information gain are presented as supportive feature reduction tools. The feature reduction process applied is based on decision tree pruning and backward elimination. This paper starts with an analysis of the KDD Cup '99 datasets and their potential for feature reduction. The dataset consists of connection records with 41 features whose relevance for intrusion detection are not clear. All traffic is either classified `normal' or into the four attack types denial-of-service, network probe, remote-to-local or user-to-root. Using our custom feature selection process, we show how we can significantly reduce the number features in the dataset to a few salient features. We conclude by presenting minimal sets with 4--8 salient features for two-class and multi-class categorisation for detecting intrusions, as well as for the detection of individual attack classes; the performance using a static classifier compares favourably to the performance using all features available. The suggested process is of general nature and can be applied to any similar dataset.

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