PLncWX: A Machine-Learning Algorithm for Plant lncRNA Identification Based on WOA-XGBoost

Long noncoding RNAs (lncRNAs) are a class of RNAs longer than 200 nt and cannot encode the protein. Studies have shown that lncRNAs can regulate gene expression at the epigenetic, transcriptional, and posttranscriptional levels, which are not only closely related to the occurrence, development, and prevention of human diseases, but also can regulate plant flowering and participate in plant abiotic stress responses such as drought and salt. Therefore, how to accurately and efficiently identify lncRNAs is still an essential job of relevant researches. There have been a large number of identification tools based on machine-learning and deep learning algorithms, mostly using human and mouse gene sequences as training sets, seldom plants, and only using one or one class of feature selection methods after feature extraction. We developed an identification model containing dicot, monocot, algae, moss, and fern. After comparing 20 feature selection methods (seven filter and thirteen wrapper methods) combined with seven classifiers, respectively, considering the correlation between features and model redundancy at the same time, we found that the WOA-XGBoost-based model had better performance with 91.55%, 96.78%, and 91.68% of accuracy, AUC, and F1_score. Meanwhile, the number of elements in the feature subset was reduced to 23, which effectively improved the prediction accuracy and modeling efficiency.

Download Full-text

An Empirical Evaluation of Feature Selection Methods

Improving Knowledge Discovery through the Integration of Data Mining Techniques - Advances in Data Mining and Database Management ◽

10.4018/978-1-4666-8513-0.ch012 ◽

2015 ◽

pp. 233-258 ◽

Cited By ~ 1

Author(s):

Mohsin Iqbal ◽

Saif Ur Rehman ◽

Saira Gillani ◽

Sohail Asghar

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Classification Accuracy ◽

Information Gain ◽

Learning Algorithm ◽

Empirical Evaluation ◽

Machine Learning Algorithms ◽

Selection Methods ◽

The One ◽

Processing And Storage

The key objective of the chapter would be to study the classification accuracy, using feature selection with machine learning algorithms. The dimensionality of the data is reduced by implementing Feature selection and accuracy of the learning algorithm improved. We test how an integrated feature selection could affect the accuracy of three classifiers by performing feature selection methods. The filter effects show that Information Gain (IG), Gain Ratio (GR) and Relief-f, and wrapper effect show that Bagging and Naive Bayes (NB), enabled the classifiers to give the highest escalation in classification accuracy about the average while reducing the volume of unnecessary attributes. The achieved conclusions can advise the machine learning users, which classifier and feature selection methods to use to optimize the classification accuracy, and this can be important, especially at risk-sensitive applying Machine Learning whereas in the one of the aim to reduce costs of collecting, processing and storage of unnecessary data.

Download Full-text

Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass

Forests ◽

10.3390/f12020216 ◽

2021 ◽

Vol 12 (2) ◽

pp. 216

Author(s):

Mi Luo ◽

Yifu Wang ◽

Yunhong Xie ◽

Lai Zhou ◽

Jingjing Qiao ◽

...

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Random Forests ◽

Aboveground Biomass ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Machine Learning Algorithm ◽

Selection Methods ◽

Estimation Models

Increasing numbers of explanatory variables tend to result in information redundancy and “dimensional disaster” in the quantitative remote sensing of forest aboveground biomass (AGB). Feature selection of model factors is an effective method for improving the accuracy of AGB estimates. Machine learning algorithms are also widely used in AGB estimation, although little research has addressed the use of the categorical boosting algorithm (CatBoost) for AGB estimation. Both feature selection and regression for AGB estimation models are typically performed with the same machine learning algorithm, but there is no evidence to suggest that this is the best method. Therefore, the present study focuses on evaluating the performance of the CatBoost algorithm for AGB estimation and comparing the performance of different combinations of feature selection methods and machine learning algorithms. AGB estimation models of four forest types were developed based on Landsat OLI data using three feature selection methods (recursive feature elimination (RFE), variable selection using random forests (VSURF), and least absolute shrinkage and selection operator (LASSO)) and three machine learning algorithms (random forest regression (RFR), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost)). Feature selection had a significant influence on AGB estimation. RFE preserved the most informative features for AGB estimation and was superior to VSURF and LASSO. In addition, CatBoost improved the accuracy of the AGB estimation models compared with RFR and XGBoost. AGB estimation models using RFE for feature selection and CatBoost as the regression algorithm achieved the highest accuracy, with root mean square errors (RMSEs) of 26.54 Mg/ha for coniferous forest, 24.67 Mg/ha for broad-leaved forest, 22.62 Mg/ha for mixed forests, and 25.77 Mg/ha for all forests. The combination of RFE and CatBoost had better performance than the VSURF–RFR combination in which random forests were used for both feature selection and regression, indicating that feature selection and regression performed by a single machine learning algorithm may not always ensure optimal AGB estimation. It is promising to extending the application of new machine learning algorithms and feature selection methods to improve the accuracy of AGB estimates.

Download Full-text

Machine Learning Based Supervised Feature Selection Algorithm for Data Mining

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j9483.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 3396-3401 ◽

Cited By ~ 1

Keyword(s):

Machine Learning ◽

Data Mining ◽

Feature Selection ◽

Learning Algorithm ◽

Modern World ◽

Feature Subset ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

Minimum Number ◽

Preprocessing Technique

Data Scientists focus on high dimensional data to predict and reveal some interesting patterns as well as most useful information to the modern world. Feature Selection is a preprocessing technique which improves the accuracy and efficiency of mining algorithms. There exist a numerous feature selection algorithms. Most of the algorithms failed to give better mining results as the scale increases. In this paper, feature selection for supervised algorithms in data mining are considered and given an overview of existing machine learning algorithm for supervised feature selection. This paper introduces an enhanced supervised feature selection algorithm which selects the best feature subset by eliminating irrelevant features using distance correlation and redundant features using symmetric uncertainty. The experimental results show that the proposed algorithm provides better classification accuracy and selects minimum number of features.

Download Full-text

Performance evaluation of random forest with feature selection methods in prediction of diabetes

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i1.pp353-359 ◽

2020 ◽

Vol 10 (1) ◽

pp. 353

Author(s):

Raghavendra S ◽

Santosh Kumar J

Keyword(s):

Machine Learning ◽

Data Mining ◽

Feature Selection ◽

Random Forest ◽

Evaluation Method ◽

Learning Algorithm ◽

Selection Methods ◽

Forward Selection ◽

Data Set ◽

Prediction Of Diabetes

<p>Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the PIMA Indian Diabtes data set of UCI repository using machine learning algorithm like Random Forest along with feature selection methods such as forward selection and backward elimination based on entropy evaluation method using percentage split as test option. The experiment was conducted using R studio platform and we achieved classification accuracy of 84.1%. From results we can say that Random Forest predicts diabetes better than other techniques with less number of attributes so that one can avoid least important test for identifying diabetes.</p>

Download Full-text

Performance Assessment of Multiple Classifiers Based on Ensemble Feature Selection Scheme for Sentiment Analysis

Applied Computational Intelligence and Soft Computing ◽

10.1155/2018/8909357 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 4

Author(s):

Monalisa Ghosh ◽

Goutam Sanyal

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Sentiment Analysis ◽

Gini Index ◽

Feature Vector ◽

Information Gain ◽

Feature Subset ◽

Selection Methods ◽

Prominent Feature ◽

Chi Square

Sentiment classification or sentiment analysis has been acknowledged as an open research domain. In recent years, an enormous research work is being performed in these fields by applying various numbers of methodologies. Feature generation and selection are consequent for text mining as the high-dimensional feature set can affect the performance of sentiment analysis. This paper investigates the inability or incompetency of the widely used feature selection methods (IG, Chi-square, and Gini Index) with unigram and bigram feature set on four machine learning classification algorithms (MNB, SVM, KNN, and ME). The proposed methods are evaluated on the basis of three standard datasets, namely, IMDb movie review and electronics and kitchen product review dataset. Initially, unigram and bigram features are extracted by applying n-gram method. In addition, we generate a composite features vector CompUniBi (unigram + bigram), which is sent to the feature selection methods Information Gain (IG), Gini Index (GI), and Chi-square (CHI) to get an optimal feature subset by assigning a score to each of the features. These methods offer a ranking to the features depending on their score; thus a prominent feature vector (CompIG, CompGI, and CompCHI) can be generated easily for classification. Finally, the machine learning classifiers SVM, MNB, KNN, and ME used prominent feature vector for classifying the review document into either positive or negative. The performance of the algorithm is measured by evaluation methods such as precision, recall, and F-measure. Experimental results show that the composite feature vector achieved a better performance than unigram feature, which is encouraging as well as comparable to the related research. The best results were obtained from the combination of Information Gain with SVM in terms of highest accuracy.

Download Full-text

Feature Selection Algorithms for Classification and Clustering

Ubiquitous Machine Learning and Its Applications - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-2545-5.ch007 ◽

2017 ◽

pp. 143-167

Author(s):

Arvind Kumar Tiwari

Keyword(s):

Machine Learning ◽

Data Mining ◽

Feature Selection ◽

Learning Algorithm ◽

High Dimensional ◽

Filter Method ◽

Selection Methods ◽

Wrapper Method ◽

Embedded Method ◽

Selection Algorithms

Feature selection is an important topic in data mining, especially for high dimensional dataset. Feature selection is a process commonly used in machine learning, wherein subsets of the features available from the data are selected for application of learning algorithm. The best subset contains the least number of dimensions that most contribute to accuracy. Feature selection methods can be decomposed into three main classes, one is filter method, another one is wrapper method and third one is embedded method. This chapter presents an empirical comparison of feature selection methods and its algorithm. In view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enable to adequately decide which algorithm to use in certain situation. This chapter reviews several fundamental algorithms found in the literature and assess their performance in a controlled scenario.

Download Full-text

Feature Selection Algorithms for Classification and Clustering

Cognitive Analytics ◽

10.4018/978-1-7998-2460-2.ch022 ◽

2020 ◽

pp. 422-442

Author(s):

Arvind Kumar Tiwari

Keyword(s):

Machine Learning ◽

Data Mining ◽

Feature Selection ◽

Learning Algorithm ◽

High Dimensional ◽

Filter Method ◽

Selection Methods ◽

Wrapper Method ◽

Embedded Method ◽

Selection Algorithms

Download Full-text

Feature Subset Selection for Malware Detection in Smart IoT Platforms

Sensors ◽

10.3390/s21041374 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1374

Author(s):

Jemal Abawajy ◽

Abdulbasit Darem ◽

Asma A. Alhashmi

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Predictive Accuracy ◽

Malware Detection ◽

Subset Selection ◽

Feature Subset Selection ◽

Feature Subset ◽

Selection Methods ◽

Android Malware ◽

Selection Algorithms

Malicious software (“malware”) has become one of the serious cybersecurity issues in Android ecosystem. Given the fast evolution of Android malware releases, it is practically not feasible to manually detect malware apps in the Android ecosystem. As a result, machine learning has become a fledgling approach for malware detection. Since machine learning performance is largely influenced by the availability of high quality and relevant features, feature selection approaches play key role in machine learning based detection of malware. In this paper, we formulate the feature selection problem as a quadratic programming problem and analyse how commonly used filter-based feature selection methods work with emphases on Android malware detection. We compare and contrast several feature selection methods along several factors including the composition of relevant features selected. We empirically evaluate the predictive accuracy of the feature subset selection algorithms and compare their predictive accuracy and the execution time using several learning algorithms. The results of the experiments confirm that feature selection is necessary for improving accuracy of the learning models as well decreasing the run time. The results also show that the performance of the feature selection algorithms vary from one learning algorithm to another and no one feature selection approach performs better than the other approaches all the time.

Download Full-text