Flexible unsupervised feature extraction for image classification

Liu, Yang; Nie, Feiping; Gao, Quanxue; Gao, Xinbo; Han, Jungong; Shao, Ling

doi:10.1016/j.neunet.2019.03.008

Cited by 50 publications

(15 citation statements)

References 28 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feature extraction is an important topic and the basis of pattern recognition and machine learning [30]. Principal component analysis is a method of feature extraction.…”

Section: Qualitative Identification Methods For Gas Mixturementioning

confidence: 99%

Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array

Fan

Xia

et al. 2019

Sensors

View full text Add to dashboard Cite

The gas sensor array has long been a major tool for measuring gas due to its high sensitivity, quick response, and low power consumption. This goal, however, faces a difficult challenge because of the cross-sensitivity of the gas sensor. This paper presents a novel gas mixture analysis method for gas sensor array applications. The features extracted from the raw data utilizing principal component analysis (PCA) were used to complete random forest (RF) modeling, which enabled qualitative identification. Support vector regression (SVR), optimized by the particle swarm optimization (PSO) algorithm, was used to select hyperparameters C and γ to establish the optimal regression model for the purpose of quantitative analysis. Utilizing the dataset, we evaluated the effectiveness of our approach. Compared with logistic regression (LR) and support vector machine (SVM), the average recognition rate of PCA combined with RF was the highest (97%). The fitting effect of SVR optimized by PSO for gas concentration was better than that of SVR and solved the problem of hyperparameters selection.

show abstract

“…Feature extraction is an important topic and the basis of pattern recognition and machine learning [30]. Principal component analysis is a method of feature extraction.…”

Section: Qualitative Identification Methods For Gas Mixturementioning

confidence: 99%

Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array

Fan

Xia

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Feature selection or gene selection is a popular and powerful approach in medical datasets to overcome this shortcoming [33][34][35]. In gene selection, to decrease the microarray data dimensions, by eliminating the irrelevant and similar genes, only a subset of relevant and dissimilar genes that are strongly related to the objective function are selected [36]. This is a powerful strategy to increase the efficiency of the machine learning algorithm, reduce time complexity, build more general classification algorithm, and reduce storage requirements [37,38].…”

Section: -3 Feature Selectionmentioning

confidence: 99%

Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis

Ataei

Majidi

Zahiri

et al. 2021

Preprint

View full text Add to dashboard Cite

Most of the current cancer treatment approaches are invasive along with a broad spectrum of side effects. Furthermore, cancer drug resistance known as chemoresistance is a huge obstacle during treatment. This study aims to predict the resistance of several cancer cell-lines to a drug known as Cisplatin. In this papers the NCBI GEO database was used to obtain data and then the harvested data was normalized and its batch effects were corrected by the Combat software. In order to select the appropriate features for machine learning, the feature selection/reduction was performed based on the Fisher Score method. Six different algorithms were then used as machine learning algorithms to detect Cisplatin resistant and sensitive samples in cancer cell lines. Moreover, Differentially Expressed Genes (DEGs) between all the sensitive and resistance samples were harvested. The selected genes were enriched in biological pathways by the enrichr database. Topological analysis was then performed on the constructed networks using Cytoscape software. Finally, the biological description of the output genes from the performed analyses was investigated through literature review. Among the six classifiers which were trained to distinguish between cisplatin resistance samples and the sensitive ones, the KNN and the Naïve Bayes algorithms were proposed as the most convenient machines according to some calculated measures. Furthermore, the results of the systems biology analysis determined several potential chemoresistance genes among which PTGER3, YWHAH, CTNNB1, ANKRD50, EDNRB, ACSL6, IFNG and, CTNNB1 are topologically more important than others. These predictions pave the way for further experimental researches.

show abstract

“…Both methods for the reduction of dimensionality are designed to improve learning efficiency, minimize computational complexity, develop more generalizable models, and reduce needed storage [6][7][8]. Feature selection has been an active research area in data mining, pattern recognition, and statistics communities [9]. The total search space to find the most relevant and non-redundant features, including all possible subsets, is 2 , where is the number of original features.…”

Section: Introductionmentioning

confidence: 99%

A Novel Community Detection Based Genetic Algorithm for Feature Selection

Rostami

Berahmand

Forouzandeh

2020

Preprint

View full text Add to dashboard Cite

The selection of features is an essential data preprocessing stage in data mining. The core principle of feature selection seems to be to pick a subset of possible features by excluding features with almost no predictive information as well as highly associated redundant features. In the past several years, a variety of meta-heuristic methods were introduced to eliminate redundant and irrelevant features as much as possible from high-dimensional datasets. Among the main disadvantages of present meta-heuristic based approaches is that they are often neglecting the correlation between a set of selected features. In this article, for the purpose of feature selection, the authors propose a genetic algorithm based on community detection, which functions in three steps. The feature similarities are calculated in the first step. The features are classified by community detection algorithms into clusters throughout the second step. In the third step, features are picked by a genetic algorithm with a new community-based repair operation. Nine benchmark classification problems were analyzed in terms of the performance of the presented approach. Also, the authors have compared the efficiency of the proposed approach with the findings from four available algorithms for feature selection. The findings indicate that the new approach continuously yields improved classification accuracy.

show abstract

Flexible unsupervised feature extraction for image classification

Cited by 50 publications

References 28 publications

Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array

Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array

Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis

A Novel Community Detection Based Genetic Algorithm for Feature Selection

Contact Info

Product

Resources

About