Research of Red Tide Algae Images Feature Selection Method Based on ReliefF and SBS

2014 ◽  
Vol 507 ◽  
pp. 806-809
Author(s):  
Shu Fang Li ◽  
Qin Jia ◽  
Hong Liang
Keyword(s):  
Feature Selection ◽  
Red Tide ◽  
Feature Selection Method ◽  
Original Data ◽  
Feature Subset ◽  
Data Set ◽  
Before And After ◽  
Optimal Feature Subset ◽  
Optimal Feature ◽  
Original Feature

In order to Red Tide algae present real-time automatic classification method of high accuracy rate, this paper proposes using ReliefF-SBS for feature selection. Namely feature analysis about Red Tide algae image original data set. And on this basis, feature selection to remove the irrelevant features and redundant features from the original feature set feature, to get the optimal feature subset, and reduce their impact on the classification accuracy. Meanwhile compare the classification results before and after SVM and KNN two kinds feature selection classifiers.

A Hybrid Feature Selection Method for Effective Data Classification in Data Mining Applications

2019 ◽  
Vol 11 (1) ◽  
pp. 1-16
Author(s):  
Ilangovan Sangaiya ◽  
A. Vincent Antony Kumar
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Original Data ◽  
Selection Method ◽  
Feature Subset ◽  
Data Set ◽  
Optimal Feature Subset ◽  
Optimal Feature

In data mining, people require feature selection to select relevant features and to remove unimportant irrelevant features from a original data set based on some evolution criteria. Filter and wrapper are the two methods used but here the authors have proposed a hybrid feature selection method to take advantage of both methods. The proposed method uses symmetrical uncertainty and genetic algorithms for selecting the optimal feature subset. This has been done so as to improve processing time by reducing the dimension of the data set without compromising the classification accuracy. This proposed hybrid algorithm is much faster and scales well to the data set in terms of selected features, classification accuracy and running time than most existing algorithms.

scholarly journals Accelerated Simulated Annealing and Mutation Operator Feature Selection method for Big Data

2019 ◽  
Vol 8 (2) ◽  
pp. 910-916
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Feature Selection Method ◽  
Classification Problem ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Mutation Operator ◽  
Knn Classifier ◽  
Optimal Feature Subset ◽  
Optimal Feature

The optimal feature subset selection over very high dimensional data is a vital issue. Even though the optimal features are selected, the classification of those selected features becomes a key complicated task. In order to handle these problems, a novel, Accelerated Simulated Annealing and Mutation Operator (ASAMO) feature selection algorithm is suggested in this work. For solving the classification problem, the Fuzzy Minimal Consistent Class Subset Coverage (FMCCSC) problem is introduced. In FMCCSC, consistent subset is combined with the K-Nearest Neighbour (KNN) classifier known as FMCCSC-KNN classifier. The two data sets Dorothea and Madelon from UCI machine repository are experimented for optimal feature selection and classification. The experimental results substantiate the efficiency of proposed ASAMO with FMCCSC-KNN classifier compared to Particle Swarm Optimization (PSO) and Accelerated PSO feature selection algorithms.

scholarly journals iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongxu Zhao ◽  
Zhixia Teng ◽  
Yanjuan Li ◽  
Dong Chen
Keyword(s):  
Feature Selection ◽  
Amino Acid ◽  
Random Forest ◽  
Feature Selection Method ◽  
Selection Strategy ◽  
Feature Subset ◽  
Evolutionary Analysis ◽  
Anti Inflammatory ◽  
Optimal Feature Subset ◽  
Optimal Feature

Recently, several anti-inflammatory peptides (AIPs) have been found in the process of the inflammatory response, and these peptides have been used to treat some inflammatory and autoimmune diseases. Therefore, identifying AIPs accurately from a given amino acid sequences is critical for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics and the acceleration of their application in therapy. In this paper, a random forest-based model called iAIPs for identifying AIPs is proposed. First, the original samples were encoded with three feature extraction methods, including g-gap dipeptide composition (GDC), dipeptide deviation from the expected mean (DDE), and amino acid composition (AAC). Second, the optimal feature subset is generated by a two-step feature selection method, in which the feature is ranked by the analysis of variance (ANOVA) method, and the optimal feature subset is generated by the incremental feature selection strategy. Finally, the optimal feature subset is inputted into the random forest classifier, and the identification model is constructed. Experiment results showed that iAIPs achieved an AUC value of 0.822 on an independent test dataset, which indicated that our proposed model has better performance than the existing methods. Furthermore, the extraction of features for peptide sequences provides the basis for evolutionary analysis. The study of peptide identification is helpful to understand the diversity of species and analyze the evolutionary history of species.

A Multilevel Optimal Feature Selection and Ensemble Learning for a Specific CAD System-Pulmonary Nodule Detection

2013 ◽  
Vol 380-384 ◽  
pp. 1593-1599
Author(s):  
Hao Yan Guo ◽  
Da Zheng Wang
Keyword(s):  
Feature Selection ◽  
Learning Algorithm ◽  
Feature Selection Method ◽  
Feature Subset ◽  
Cad System ◽  
Optimal Feature Selection ◽  
Optimal Feature Subset ◽  
Feature Selection Approach ◽  
Selection Algorithms ◽  
Optimal Feature

The traditional motivation behind feature selection algorithms is to find the best subset of features for a task using one particular learning algorithm. However, it has been often found that no single classifier is entirely satisfactory for a particular task. Therefore, how to further improve the performance of these single systems on the basis of the previous optimal feature subset is a very important issue.We investigate the notion of optimal feature selection and present a practical feature selection approach that is based on an optimal feature subset of a single CAD system, which is referred to as a multilevel optimal feature selection method (MOFS) in this paper. Through MOFS, we select the different optimal feature subsets in order to eliminate features that are redundant or irrelevant and obtain optimal features.

Feature distillation and accumulated selection for automated fraudulent publisher classification from user click data of online advertising

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Deepti Sisodia ◽  
Dilip Singh Sisodia
Keyword(s):  
Feature Selection ◽  
Online Advertising ◽  
Feature Selection Method ◽  
Majority Voting ◽  
Feature Subset ◽  
Relevant Feature ◽  
Selection Methods ◽  
Content Type ◽  
Optimal Feature Subset ◽  
Optimal Feature

PurposeThe problem of choosing the utmost useful features from hundreds of features from time-series user click data arises in online advertising toward fraudulent publisher's classification. Selecting feature subsets is a key issue in such classification tasks. Practically, the use of filter approaches is common; however, they neglect the correlations amid features. Conversely, wrapper approaches could not be applied due to their complexities. Moreover, in particular, existing feature selection methods could not handle such data, which is one of the major causes of instability of feature selection.Design/methodology/approachTo overcome such issues, a majority voting-based hybrid feature selection method, namely feature distillation and accumulated selection (FDAS), is proposed to investigate the optimal subset of relevant features for analyzing the publisher's fraudulent conduct. FDAS works in two phases: (1) feature distillation, where significant features from standard filter and wrapper feature selection methods are obtained using majority voting; (2) accumulated selection, where we enumerated an accumulated evaluation of relevant feature subset to search for an optimal feature subset using effective machine learning (ML) models.FindingsEmpirical results prove enhanced classification performance with proposed features in average precision, recall, f1-score and AUC in publisher identification and classification.Originality/valueThe FDAS is evaluated on FDMA2012 user-click data and nine other benchmark datasets to gauge its generalizing characteristics, first, considering original features, second, with relevant feature subsets selected by feature selection (FS) methods, third, with optimal feature subset obtained by the proposed approach. ANOVA significance test is conducted to demonstrate significant differences between independent features.

scholarly journals Feature Selection using Genetic Algorithm for Clustering high Dimensional Data

2018 ◽  
Vol 7 (2.11) ◽  
pp. 27 ◽  
Author(s):  
Kahkashan Kouser ◽  
Amrita Priyam
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
Feature Space ◽  
High Dimensional ◽  
Feature Subset ◽  
Data Set ◽  
Optimal Feature Subset ◽  
Optimal Feature

One of the open problems of modern data mining is clustering high dimensional data. For this in the paper a new technique called GA-HDClustering is proposed, which works in two steps. First a GA-based feature selection algorithm is designed to determine the optimal feature subset; an optimal feature subset is consisting of important features of the entire data set next, a K-means algorithm is applied using the optimal feature subset to find the clusters. On the other hand, traditional K-means algorithm is applied on the full dimensional feature space.    Finally, the result of GA-HDClustering  is  compared  with  the  traditional  clustering  algorithm.  For comparison different validity  matrices  such  as  Sum  of  squared  error  (SSE),  Within  Group average distance (WGAD), Between group distance (BGD), Davies-Bouldin index(DBI),   are used .The GA-HDClustering uses genetic algorithm for searching an effective feature subspace in a large feature space. This large feature space is made of all dimensions of the data set. The experiment performed on the standard data set revealed that the GA-HDClustering is superior to traditional clustering algorithm. 

Modulation Recognition of Digital Multimedia Signal Based on Data Feature Selection

2017 ◽  
Vol 8 (3) ◽  
pp. 90-111 ◽  
Author(s):  
Hui Wang ◽  
Li Li Guo ◽  
Yun Lin
Keyword(s):  
Feature Selection ◽  
Information Entropy ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Digital Multimedia ◽  
Optimal Feature Subset ◽  
Optimal Feature

Automatic modulation recognition is very important for the receiver design in the broadband multimedia communication system, and the reasonable signal feature extraction and selection algorithm is the key technology of Digital multimedia signal recognition. In this paper, the information entropy is used to extract the single feature, which are power spectrum entropy, wavelet energy spectrum entropy, singular spectrum entropy and Renyi entropy. And then, the feature selection algorithm of distance measurement and Sequential Feature Selection(SFS) are presented to select the optimal feature subset. Finally, the BP neural network is used to classify the signal modulation. The simulation result shows that the four-different information entropy can be used to classify different signal modulation, and the feature selection algorithm is successfully used to choose the optimal feature subset and get the best performance.

scholarly journals Intrusion Detection System using SMIFS and Multi class Multi layer Perceptron

2019 ◽  
Vol 8 (9) ◽  
pp. 2622-2628
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Mutual Information ◽  
New Technologies ◽  
Detection System ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Feature Subset ◽  
Classification Problems ◽  
Data Set

As the new technologies are emerging, data is getting generated in larger volumes high dimensions. The high dimensionality of data may rise to great challenge while classification. The presence of redundant features and noisy data degrades the performance of the model. So, it is necessary to extract the relevant features from given data set. Feature extraction is an important step in many machine learning algorithms. Many researchers have been attempted to extract the features. Among these different feature extraction methods, mutual information is widely used feature selection method because of its good quality of quantifying dependency among the features in classification problems. To cope with this issue, in this paper we proposed simplified mutual information based feature selection with less computational overhead. The selected feature subset is experimented with multilayered perceptron on KDD CUP 99 data set with 2- class classification, 5-class classification and 4-class classification. The accuracy is of these models almost similar with less number of features.

scholarly journals Optimal Feature Subset Selection for Imbalanced Class Data using SMOTE and Binary ALO Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 344-349
Keyword(s):  
Feature Selection ◽  
Class Imbalance ◽  
Classification Performance ◽  
Selection Model ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Spatial Features ◽  
Imbalanced Classes ◽  
Optimal Feature Subset ◽  
Optimal Feature

Feature selection in multispectral high dimensional information is a hard labour machine learning problem because of the imbalanced classes present in the data. The existing Most of the feature selection schemes in the literature ignore the problem of class imbalance by choosing the features from the classes having more instances and avoiding significant features of the classes having less instances. In this paper, SMOTE concept is exploited to produce the required samples form minority classes. Feature selection model is formulated with the objective of reducing number of features with improved classification performance. This model is based on dimensionality reduction by opt for a subset of relevant spectral, textural and spatial features while eliminating the redundant features for the purpose of improved classification performance. Binary ALO is engaged to solve the feature selection model for optimal selection of features. The proposed ALO-SVM with wrapper concept is applied to each potential solution obtained during optimization step. The working of this methodology is tested on LANDSAT multispectral image.

scholarly journals A Novel Feature Selection Method Based on Maximum Likelihood Logistic Regression for Imbalanced Learning in Software Defect Prediction

2020 ◽  
Vol 17 (5) ◽  
pp. 721-730
Author(s):  
Kamal Bashir ◽  
Tianrui Li ◽  
Mahama Yahaya
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Feature Selection ◽  
Maximum Likelihood ◽  
Defect Prediction ◽  
Feature Subset ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Optimal Feature Subset ◽  
Optimal Feature

The most frequently used machine learning feature ranking approaches failed to present optimal feature subset for accurate prediction of defective software modules in out-of-sample data. Machine learning Feature Selection (FS) algorithms such as Chi-Square (CS), Information Gain (IG), Gain Ratio (GR), RelieF (RF) and Symmetric Uncertainty (SU) perform relatively poor at prediction, even after balancing class distribution in the training data. In this study, we propose a novel FS method based on the Maximum Likelihood Logistic Regression (MLLR). We apply this method on six software defect datasets in their sampled and unsampled forms to select useful features for classification in the context of Software Defect Prediction (SDP). The Support Vector Machine (SVM) and Random Forest (RaF) classifiers are applied on the FS subsets that are based on sampled and unsampled datasets. The performance of the models captured using Area Ander Receiver Operating Characteristics Curve (AUC) metrics are compared for all FS methods considered. The Analysis Of Variance (ANOVA) F-test results validate the superiority of the proposed method over all the FS techniques, both in sampled and unsampled data. The results confirm that the MLLR can be useful in selecting optimal feature subset for more accurate prediction of defective modules in software development process

Sign in / Sign up

Export Citation Format

Share Document