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feature selection algorithm in Classification Learning Using Support Vector Machines

COMPUTATIONAL MATHEMATICS AND MATHEMATICAL PHYSICS 2008年第7期48卷 1243-1260页

作者： Goncharov, Yu. V. Muchnik, I. B. Shvartser, L. V. Russian Acad Sci Dorodnicyn Comp Ctr Moscow 119333 Russia Rutgers State Univ New Brunswick NJ 08903 USA Ness Technol Atidim IL-61581 Tel Aviv Israel

An algorithm for selecting features in the classification learning problem is considered. The algorithm is based on a modification of the standard criterion used in the support vector machine method. The new criterion adds to the standard criterion a penalty function that depends on the selected features. The solution of the problem is reduced to finding the minimax of a convex-concave function. As a result, the initial set of features is decomposed into three classes-unconditionally selected, weighted selected, and eliminated features.

关键词： feature selection algorithm classification learning support vector machine saddle point searching algorithm

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Prediction of soil clay minerals from some soil properties with use of feature selection algorithm and ANFIS methods

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SOIL RESEARCH 2019年第7期57卷 788-796页

作者： Najafi-Ghiri, Mahdi Mokarram, Marzieh Owliaie, Hamid Reza Shiraz Univ Coll Agr & Nat Resources Darab Dept Soil Sci Shiraz Iran Shiraz Univ Coll Agr & Nat Resources Darab Dept Range & Watershed Management Shiraz Iran Univ Yasuj Coll Agr Dept Soil Sci Yasuj Iran

Researchers use different methods to investigate and quantify clay minerals. X-ray diffraction is a common and widespread approach for clay mineralogy investigation, but is time-consuming and expensive, especially in highly calcareous soils. The aim of this research was prediction of clay minerals in calcareous soils of southern Iran using a feature selection algorithm and adaptive neuro-fuzzy inference system (ANFIS) methods. Fifty soil samples from different climatic regions of southern Iran were collected and different climatic, soil properties and clay minerals were determined using X-ray diffraction. feature selection algorithms were used for selection of the best feature subset for prediction of clay mineral types along with two sets of training and testing data. Results indicated that the best feature subset by Best-First for prediction of illite was cation exchange capacity (CEC), sand, total potassium, silt and agroclimatic index (correlation coefficient (R) = 0.99 for training and testing data);for smectite was precipitation, temperature, evapotranspiration and CEC (R = 0.89 and 0.87 for training and testing data respectively);and for palygorskite was precipitation, temperature, evapotranspiration and calcium carbonate equivalent (CCE) (R = 0.98 for training and testing data). An attempt was made to predict clay minerals type by ANFIS using selected data from the feature selection algorithm. The evaluation of method by calculating root mean square error (RMSE), mean absolute error (MAE) and R indicated that the ANFIS method may be suitable for illite, chlorite, smectite and palygorskite prediction (RMSE, MAE and R of 0.001-0.028, 0.004-0.012 and 0.67-0.89 respectively for training and testing data). Comparison of data for all clay minerals showed that ANFIS method did not predict illite and chlorite as well as other minerals in the studied soils.

关键词： ANFIS method clay mineralogy feature selection algorithm soil properties

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An Unsupervised feature selection algorithm with feature Ranking for Maximizing Performance of the Classifiers

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International Journal of Automation and computing 2015年第5期12卷 511-517页

作者： Danasingh Asir Antony Gnana Singh Subramanian Appavu Alias Balamurugan Epiphany Jebamalar Leavline Department of Computer Science and Engineering Bharathidasan Institute of Technology Anna University Department of Information Technology K.L.N College of Information Technology Department of Electronics and Communication Engineering Bharathidasan Institute of Technology Anna University

Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy,efforts, money and time. The right decision prevents physical and material losses and it is practiced in all the fields including medical,finance, environmental studies, engineering and emerging technologies. Prediction is carried out by a model called classifier. The predictive accuracy of the classifier highly depends on the training datasets utilized for training the classifier. The irrelevant and redundant features of the training dataset reduce the accuracy of the classifier. Hence, the irrelevant and redundant features must be removed from the training dataset through the process known as feature selection. This paper proposes a feature selection algorithm namely unsupervised learning with ranking based feature selection(FSULR). It removes redundant features by clustering and eliminates irrelevant features by statistical measures to select the most significant features from the training dataset. The performance of this proposed algorithm is compared with the other seven feature selection algorithms by well known classifiers namely naive Bayes(NB),instance based(IB1) and tree based J48. Experimental results show that the proposed algorithm yields better prediction accuracy for classifiers.

关键词： feature selection algorithm classification cluster

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Classification Performance-Based feature selection algorithm for Machine Learning: P-Score

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IRBM 2020年第4期41卷 229-239页

作者： Ucar, M. K. Sakarya Univ Fac Engn Elect Elect Engn Sakarya Turkey

feature selection algorithms are the cornerstone of machine learning. By increasing the properties of the samples and samples, the feature selection algorithm selects the significant features. The general name of the methods that perform this function is the feature selection algorithm. The general purpose of feature selection algorithms is to select the most relevant properties of data classes and to increase the classification performance. Thus, we can select features based on their classification performance. In this study, we have developed a feature selection algorithm based on decision support vectors classification performance. The method can work according to two different selection criteria. We tested the classification performances of the features selected with P-Score with three different classifiers. Besides, we assessed P-Score performance with 13 feature selection algorithms in the literature. According to the results of the study, the P-Score feature selection algorithm has been determined as a method which can be used in the field of machine learning. (C) 2020 AGBM. Published by Elsevier Masson SAS. All rights reserved.

关键词： Machine learning feature selection algorithm Classification P-Score

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feature selection algorithm using neighborhood equivalence tolerance relation for incomplete decision systems

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APPLIED SOFT COMPUTING 2024年 157卷

作者： Wu, Shangzhi Wang, Litai Ge, Shuyue Xiong, Zheng Liu, Jie Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China

Rough set is an important method for dealing with incomplete information systems. In incomplete information systems, the most common way to determine the relation between two samples is the tolerance relation. However, the condition for the tolerance relation to determine those samples may belong to the same category is very lenient, which makes the reduction rate low when using the rough set generated by this relation to select features. In response to the above problems, we design the neighborhood equivalence tolerance relation to solve them. Different from other improved tolerance relations, firstly, the relation designed in this paper does not require additional threshold to accomplish the above goals, which will avoid the trouble caused by the given threshold. Secondly, we notice that most of the current improvements for this kind of problems are computationally cumbersome, and the relation designed in this paper is simple and effective. Based on this, we construct a neighborhood rough set model that handles incomplete information by using this relation, introduce its properties, expound the properties that a reduction set should satisfy, quantify the importance of conditional attributes with attribute dependence degree, which provides the basis for the design of feature selection algorithm. Finally, the greedy strategy is used to design a forward feature selection algorithm. Experimental results show that the model is effective in dealing with incomplete information systems. The feature selection algorithm has the smallest size of the average reduced subset on twelve datasets, and maintains the accuracy of the classifier, which verifies that the feature selection algorithm can effectively deal with incomplete information systems.

关键词： Rough set Incomplete information systems Neighborhood equivalence tolerance relation feature selection algorithm

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An Heuristic feature selection algorithm to Evaluate Academic Performance of Students 10

An Heuristic Feature Selection Algorithm to Evaluate Academi...

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IEEE 10th Control and System Graduate Research Colloquium (ICSGRC)

作者： Ajibade, Samuel-Soma M. Ahmad, Nor Bahiah Shamsuddin, Siti Mariyam UTM Comp Sci Dept Johor Baharu Malaysia

ISBN: (纸本)9781728107554

The value of schooling and academic performance of student is the topmost priority of all academic institutions. Educational Data Mining (EDM) is an evolving area of research which aids academic institutions to enhance their student's performances. feature selection algorithms eradicates inapt and unrelated data from the dataset, thereby increasing the classifiers performances that are utilized in EDM. This aim of this paper is to evaluate the performance of students utilizing a heuristic technique known as Differential Evolution for feature selection algorithms on the dataset of students and some other feature selection algorithms have also been used which have never been used before on the dataset. Also, classification techniques such as Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN) and Discriminant Analysis (DISC) were used to evaluate. The Differential Evolution (DE) algorithm is proposed as a better feature selection algorithm for evaluating the academic performance of students and this gave a better accuracy than other feature selection algorithm that were used. The outcome of the different feature selection algorithms and classification techniques will help researchers to find the finest combinations of the classifiers and feature selection algorithms. This paper is a step towards playing an important role in enhancing the standard of education in academic institutions and also to carefully guide researchers in strategically interfering in academic issues.

关键词： educational data mining differential evolution student performance feature selection algorithm classification algorithm

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RESEARCH ON feature selection algorithm BASED ON MUTUAL INFORMATION AND GENETIC algorithm 11

RESEARCH ON FEATURE SELECTION ALGORITHM BASED ON MUTUAL INFO...

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11th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Tang, Pan-Shi Tang, Xiao-Long Tao, Zhong-Yu Li, Jian-Ping Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Chongqing Key Lab Comp Network & Commun Technol Chongqing Peoples R China

ISBN: (纸本)9781479972081

The wide application of Internet technology and media technology produces more and more data which also leads the arrival of the era of big data. However, it is difficult to extract the needed information from the original data directly except some special conditions. In recent years, the development of machine learning which provide a effective way to solve this problem for us. You can obtain lower rate of Miscalculate when you select a reasonable feature selection algorithm under the premise of not increasing the complexity of algorithm. At present it is divided into two categories named the Filter and Wrapper feature selection algorithm in the field of machine learning. This paper considers both the advantages and disadvantages of these two feature selection algorithm and studies the combined feature selection algorithm.

关键词： Machine learning feature selection algorithm Filter Wrapper

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Performance Analysis of feature selection algorithm for Educational Data Mining

Performance Analysis of Feature Selection Algorithm for Educ...

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IEEE Conference on Big Data and Analytics (ICBDA)

作者： Zaffar, Maryam Hashmani, Manzoor Ahmed Savita, K. S. Univ Teknol PETRONAS Dept Comp & Informat Sci Seri Iskandar Malaysia

ISBN: (纸本)9781538607909

Student's academic performance is the main focus of all educational institutions. Educational Data Mining (EDM) is an emerging research area help the educational institutions to improve the performance of their students. feature selection (FS) algorithms remove irrelevant data from the educational dataset and hence increases the performance of classifiers used in EDM techniques. This paper present an analysis of the performance of feature selection algorithms on student data set. The obtained results of the different FS algorithms and classifiers will also help the new researchers in finding the best combinations of FS algorithms and classifiers. Selecting relevant features for student prediction model is very sensitive issue for educational stakeholders, as they have to take decisions on the basis of results of prediction models. Furthermore our paper is an attempt of playing a positive role in the improvement of education quality, as well as guides new researchers in making academic intervention.

关键词： Educational data mining feature selection algorithm Classifiers

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Regional soil organic matter mapping models based on the optimal time window, feature selection algorithm and Google Earth Engine

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SOIL & TILLAGE RESEARCH 2022年 219卷 105325-105325页

作者： Luo, Chong Zhang, Xinle Wang, Yihao Men, Zhibo Liu, Huanjun Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130102 Peoples R China Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China Northeast Agr Univ Sch Econ & Management Harbin 150030 Peoples R China Northeast Agr Univ Sch Publ Adm & Law Harbin 150030 Peoples R China

The spatial distribution of soil organic matter (SOM) is highly significant to the assessment of the regional carbon balance, food security and cultivated land quality. Due to climate change and the increasing food demand, the intensity of cultivated land development in the Northeast China black soil region is increasing, and it is urgent to accurately map the SOM content in this region. Remote sensing technology has been widely applied in the field of soil mapping, but large-scale and high-precision soil mapping remains a significant challenge. In this study, the Google Earth Engine (GEE) platform is adopted to generate synthetic soil images based on Landsat-8 and Sentinel-2 images capturing bare soil periods at 20-d intervals. Then, the spectral index and band are adopted as input variables to evaluate the prediction accuracy of these synthetic images depicting different periods using random forest (RF) regression. Finally, two feature selection methods (Boruta and recursive feature elimination (RFE)) are employed to evaluate the performance of these two methods. The results indicate that 1) the optimal time window for SOM prediction is day of year (DOY) 120-140 for the Songnen Plain;2) the performance of SOM prediction based on Landsat-8 synthetic images is better than that based on Sentinel-2 synthetic images;and 3) both feature selection methods improve the SOM prediction accuracy, but RFE has the highest accuracy(Landsat-8 with Coefficient of Determination (R-2) of 0.702, Root Mean Square Error (RMSE) of 0.681%;Sentinel-2 with R-2 of 0.5963, RMSE of 0.793%). This study provides a new model for large-scale and high-spatial resolution SOM prediction and verifies the importance of the time window to the SOM prediction accuracy.

关键词： Soil organic matter Optimal time window feature selection algorithm Google Earth Engine Landsat-8 Sentinel-2

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Research and performance analysis of random forest-based feature selection algorithm in sports effectiveness evaluation

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SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Li, Yujiao Mu, Yingjie Harbin Normal Univ Harbin 150025 Peoples R China

The rapid progress in fields such as data mining and machine learning, as well as the explosive growth of sports big data, have posed new challenges to the research of sports big data. Most of the available sports data mining techniques concentrates on extracting and constructing effective features for basic sports data, which cannot be achieved simply by using data statistics. Especially in the targeted mining of sports data, traditional mining techniques still have shortcomings such as low classification accuracy and insufficient refinement. In order to solve the problem of low accuracy in traditional mining methods, the study combines the random forest algorithm with the artificial raindrop algorithm, and adopts a sports data mining method based on feature selection to achieve effective analysis of sports big data. This study is based on the evaluation method of motion effects using random forests, and uses feature extraction algorithms to study the motion effect impacts. It uses the information gain index to rank the importance of features and accurately gain the degree of influence of exercise on various indicators of the human body. Through simulation verification, the algorithm proposed by the research institute performs the best in accuracy and FI scores on the training and testing sets, with accuracies of 0.849 +/- 0.021 and 0.819 +/- 0.022, respectively, and F1 scores of 0.837 +/- 0.020 and 0.864 +/- 0.021, respectively. This indicates that the algorithm proposed by the research institute has high classification accuracy and performance proves that the Random Forest-based feature selection algorithm established in this study is superior to the existing traditional feature extraction and extraction methods in terms of both performance and accuracy. The proposal of this data analysis method has achieved accurate and efficient utilization of sports big data, which is of great significance for the development of the sports education industry.

关键词： Random forest feature selection algorithm Sports big data Sports data Data statistics

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