Literatura académica sobre el tema "FEATURE SELECTION TECHNIQUE"
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Artículos de revistas sobre el tema "FEATURE SELECTION TECHNIQUE"
Sharaff, Aakanksha, Naresh Kumar Nagwani y Kunal Swami. "Impact of Feature Selection Technique on Email Classification". International Journal of Knowledge Engineering-IACSIT 1, n.º 1 (2015): 59–63. http://dx.doi.org/10.7763/ijke.2015.v1.10.
Texto completoSalama, Mostafa A. y Ghada Hassan. "A Novel Feature Selection Measure Partnership-Gain". International Journal of Online and Biomedical Engineering (iJOE) 15, n.º 04 (27 de febrero de 2019): 4. http://dx.doi.org/10.3991/ijoe.v15i04.9831.
Texto completoSikri, Alisha, N. P. Singh y Surjeet Dalal. "Analysis of Rank Aggregation Techniques for Rank Based on the Feature Selection Technique". International Journal on Recent and Innovation Trends in Computing and Communication 11, n.º 3s (11 de marzo de 2023): 95–108. http://dx.doi.org/10.17762/ijritcc.v11i3s.6160.
Texto completoGoswami, Saptarsi, Amit Kumar Das, Amlan Chakrabarti y Basabi Chakraborty. "A feature cluster taxonomy based feature selection technique". Expert Systems with Applications 79 (agosto de 2017): 76–89. http://dx.doi.org/10.1016/j.eswa.2017.01.044.
Texto completoJain, Rahi y Wei Xu. "HDSI: High dimensional selection with interactions algorithm on feature selection and testing". PLOS ONE 16, n.º 2 (16 de febrero de 2021): e0246159. http://dx.doi.org/10.1371/journal.pone.0246159.
Texto completoRamineni, Vyshnavi y Goo-Rak Kwon. "Diagnosis of Alzheimer’s Disease using Wrapper Feature Selection Method". Korean Institute of Smart Media 12, n.º 3 (30 de abril de 2023): 30–37. http://dx.doi.org/10.30693/smj.2023.12.3.30.
Texto completoZabidi, A., W. Mansor y Khuan Y. Lee. "Optimal Feature Selection Technique for Mel Frequency Cepstral Coefficient Feature Extraction in Classifying Infant Cry with Asphyxia". Indonesian Journal of Electrical Engineering and Computer Science 6, n.º 3 (1 de junio de 2017): 646. http://dx.doi.org/10.11591/ijeecs.v6.i3.pp646-655.
Texto completoMiftahushudur, Tajul, Chaeriah Bin Ali Wael y Teguh Praludi. "Infinite Latent Feature Selection Technique for Hyperspectral Image Classification". Jurnal Elektronika dan Telekomunikasi 19, n.º 1 (31 de agosto de 2019): 32. http://dx.doi.org/10.14203/jet.v19.32-37.
Texto completoSaifan, Ahmad A. y Lina Abu-wardih. "Software Defect Prediction Based on Feature Subset Selection and Ensemble Classification". ECTI Transactions on Computer and Information Technology (ECTI-CIT) 14, n.º 2 (9 de octubre de 2020): 213–28. http://dx.doi.org/10.37936/ecti-cit.2020142.224489.
Texto completoAli, Tariq, Asif Nawaz y Hafiza Ayesha Sadia. "Genetic Algorithm Based Feature Selection Technique for Electroencephalography Data". Applied Computer Systems 24, n.º 2 (1 de diciembre de 2019): 119–27. http://dx.doi.org/10.2478/acss-2019-0015.
Texto completoTesis sobre el tema "FEATURE SELECTION TECHNIQUE"
Tan, Feng. "Improving Feature Selection Techniques for Machine Learning". Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/27.
Texto completoLoscalzo, Steven. "Group based techniques for stable feature selection". Diss., Online access via UMI:, 2009.
Buscar texto completoVege, Sri Harsha. "Ensemble of Feature Selection Techniques for High Dimensional Data". TopSCHOLAR®, 2012. http://digitalcommons.wku.edu/theses/1164.
Texto completoGustafsson, Robin. "Ordering Classifier Chains using filter model feature selection techniques". Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14817.
Texto completoZhang, Fu. "Intelligent feature selection for neural regression : techniques and applications". Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/49639/.
Texto completoMuteba, Ben Ilunga. "Data Science techniques for predicting plant genes involved in secondary metabolites production". University of the Western Cape, 2018. http://hdl.handle.net/11394/7039.
Texto completoPlant genome analysis is currently experiencing a boost due to reduced costs associated with the development of next generation sequencing technologies. Knowledge on genetic background can be applied to guide targeted plant selection and breeding, and to facilitate natural product discovery and biological engineering. In medicinal plants, secondary metabolites are of particular interest because they often represent the main active ingredients associated with health-promoting qualities. Plant polyphenols are a highly diverse family of aromatic secondary metabolites that act as antimicrobial agents, UV protectants, and insect or herbivore repellents. Most of the genome mining tools developed to understand genetic materials have very seldom addressed secondary metabolite genes and biosynthesis pathways. Little significant research has been conducted to study key enzyme factors that can predict a class of secondary metabolite genes from polyketide synthases. The objectives of this study were twofold: Primarily, it aimed to identify the biological properties of secondary metabolite genes and the selection of a specific gene, naringenin-chalcone synthase or chalcone synthase (CHS). The study hypothesized that data science approaches in mining biological data, particularly secondary metabolite genes, would enable the compulsory disclosure of some aspects of secondary metabolite (SM). Secondarily, the aim was to propose a proof of concept for classifying or predicting plant genes involved in polyphenol biosynthesis from data science techniques and convey these techniques in computational analysis through machine learning algorithms and mathematical and statistical approaches. Three specific challenges experienced while analysing secondary metabolite datasets were: 1) class imbalance, which refers to lack of proportionality among protein sequence classes; 2) high dimensionality, which alludes to a phenomenon feature space that arises when analysing bioinformatics datasets; and 3) the difference in protein sequences lengths, which alludes to a phenomenon that protein sequences have different lengths. Considering these inherent issues, developing precise classification models and statistical models proves a challenge. Therefore, the prerequisite for effective SM plant gene mining is dedicated data science techniques that can collect, prepare and analyse SM genes.
Strand, Lars Helge. "Feature selection in Medline using text and data mining techniques". Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2005. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9249.
Texto completoIn this thesis we propose a new method for searching for gene products gene products and give annotations associating genes with Gene Ontology codes. Many solutions already exists, using different techniques, however few are capable of addressing the whole GO hierarchy. We propose a method for exploring this hierarchy by dividing it into subtrees, trying to find terms that are characteristics for the subtrees involved. Using a feature selection based on chi-square analysis and naive Bayes classification to find the correct GO nodes.
Ni, Weizeng. "A Review and Comparative Study on Univariate Feature Selection Techniques". University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353156184.
Texto completoDang, Vinh Q. "Evolutionary approaches for feature selection in biological data". Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2014. https://ro.ecu.edu.au/theses/1276.
Texto completoMiller, Corey Alexander. "Intelligent Feature Selection Techniques for Pattern Classification of Time-Domain Signals". W&M ScholarWorks, 2013. https://scholarworks.wm.edu/etd/1539623620.
Texto completoLibros sobre el tema "FEATURE SELECTION TECHNIQUE"
K, Kokula Krishna Hari y K. Saravanan, eds. Exploratory Analysis of Feature Selection Techniques in Medical Image Processing. Tiruppur, Tamil Nadu, India: Association of Scientists, Developers and Faculties, 2016.
Buscar texto completoRaza, Muhammad Summair y Usman Qamar. Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4965-1.
Texto completoRaza, Muhammad Summair y Usman Qamar. Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9166-9.
Texto completoRaza, Muhammad Summair y Usman Qamar. Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. Springer, 2017.
Buscar texto completoRaza, Muhammad Summair y Usman Qamar. Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. Springer, 2019.
Buscar texto completoRaza, Muhammad Summair y Usman Qamar. Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. Springer Singapore Pte. Limited, 2020.
Buscar texto completoRaza, Muhammad Summair y Usman Qamar. Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. Springer Singapore Pte. Limited, 2018.
Buscar texto completoGrant, Stuart A. y David B. Auyong. Basic Principles of Ultrasound Guided Nerve Block. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190231804.003.0001.
Texto completoThrumurthy, Sri G., Tania S. De Silva, Zia M. Moinuddin y Stuart Enoch. EMQs for the MRCS Part A. Oxford University Press, 2013. http://dx.doi.org/10.1093/oso/9780199645640.001.0001.
Texto completoThrumurthy, Sri G., Tania Samantha De Silva, Zia Moinuddin y Stuart Enoch. SBA MCQs for the MRCS Part A. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199645633.001.0001.
Texto completoCapítulos de libros sobre el tema "FEATURE SELECTION TECHNIQUE"
Singh, Upendra y Sudhakar Tripathi. "Protein Classification Using Hybrid Feature Selection Technique". En Communications in Computer and Information Science, 813–21. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3433-6_97.
Texto completoNaveen, Nekuri y Mandala Sookshma. "Adaptive Feature Selection and Classification Using Optimization Technique". En Frontiers in Intelligent Computing: Theory and Applications, 146–55. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9186-7_17.
Texto completoGuru, D. S., Mostafa Ali y Mahamad Suhil. "A Novel Feature Selection Technique for Text Classification". En Advances in Intelligent Systems and Computing, 721–33. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1498-8_63.
Texto completoNagaraj, Naik, B. M. Vikranth y N. Yogesh. "Recursive Feature Elimination Technique for Technical Indicators Selection". En Communications in Computer and Information Science, 139–45. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08277-1_12.
Texto completoZheng, Hai-Tao y Haiyang Zhang. "Online Streaming Feature Selection Using Sampling Technique and Correlations Between Features". En Web Technologies and Applications, 43–55. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45817-5_4.
Texto completoChristy, A. y G. Meera Gandhi. "Feature Selection and Clustering of Documents Using Random Feature Set Generation Technique". En Advances in Data Science and Management, 67–79. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0978-0_6.
Texto completoLee, Kee-Cheol. "A Technique of Dynamic Feature Selection Using the Feature Group Mutual Information". En Methodologies for Knowledge Discovery and Data Mining, 138–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48912-6_19.
Texto completoLeKhac, NhienAn, Bo Wu, ChongCheng Chen y M.-Tahar Kechadi. "Feature Selection Parallel Technique for Remotely Sensed Imagery Classification". En Lecture Notes in Computer Science, 623–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39643-4_45.
Texto completoSeeja, K. R. "A Novel Feature Selection Technique for SAGE Data Classification". En Communications in Computer and Information Science, 49–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39678-6_9.
Texto completoAlharbi, Abdullah Semran, Yuefeng Li y Yue Xu. "Integrating LDA with Clustering Technique for Relevance Feature Selection". En AI 2017: Advances in Artificial Intelligence, 274–86. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63004-5_22.
Texto completoActas de conferencias sobre el tema "FEATURE SELECTION TECHNIQUE"
Battisti, Felipe de Melo y Tiago Buarque Assunção de Carvalho. "Threshold Feature Selection PCA". En Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/kdmile.2022.227718.
Texto completoBibi, K. Fathima y M. Nazreen Banu. "Feature subset selection based on Filter technique". En 2015 International Conference on Computing and Communications Technologies (ICCCT). IEEE, 2015. http://dx.doi.org/10.1109/iccct2.2015.7292710.
Texto completoWiratsin, In-On y Lalita Narupiyakul. "Feature Selection Technique for Autism Spectrum Disorder". En CCEAI 2021: 5th International Conference on Control Engineering and Artificial Intelligence. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3448218.3448241.
Texto completoTayal, Devendra K., Neha Srivastava y Neha. "Feature Selection using Enhanced Nature Optimization Technique". En 2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS). IEEE, 2023. http://dx.doi.org/10.1109/aicaps57044.2023.10074104.
Texto completoS, Abdul Razak M., Nirmala C. R, Chetan B. B, Mohammed Rafi y Sreenivasa B. R. "Online feature Selection using Pearson Correlation Technique". En 2022 IEEE 7th International Conference on Recent Advances and Innovations in Engineering (ICRAIE). IEEE, 2022. http://dx.doi.org/10.1109/icraie56454.2022.10054267.
Texto completoLópez Jaimes, Antonio, Carlos A. Coello Coello y Debrup Chakraborty. "Objective reduction using a feature selection technique". En the 10th annual conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1389095.1389228.
Texto completoWang, Yong, Adam J. Brzezinski, Xianli Qiao y Jun Ni. "Heuristic Feature Selection for Shaving Tool Wear Classification". En ASME 2016 11th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/msec2016-8547.
Texto completoMeng Wang, Shudong Sun, Ganggang Niu, Yuanzhi Tu y Shihui Guo. "A feature selection technique based on equivalent relation". En 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC). IEEE, 2011. http://dx.doi.org/10.1109/aimsec.2011.6010707.
Texto completoLiogiene, Tatjana y Gintautas Tamulevicius. "SFS feature selection technique for multistage emotion recognition". En 2015 IEEE 3rd Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE). IEEE, 2015. http://dx.doi.org/10.1109/aieee.2015.7367299.
Texto completoMary, I. Thusnavis Bella, A. Vasuki y M. A. P. Manimekalai. "An optimized feature selection CBIR technique using ANN". En 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). IEEE, 2017. http://dx.doi.org/10.1109/iceeccot.2017.8284550.
Texto completoInformes sobre el tema "FEATURE SELECTION TECHNIQUE"
Zhao, George, Grang Mei, Bulent Ayhan, Chiman Kwan y Venu Varma. DTRS57-04-C-10053 Wave Electromagnetic Acoustic Transducer for ILI of Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), marzo de 2005. http://dx.doi.org/10.55274/r0012049.
Texto completoSearcy, Stephen W. y Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, agosto de 1993. http://dx.doi.org/10.32747/1993.7568747.bard.
Texto completoTayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, enero de 2022. http://dx.doi.org/10.31979/mti.2022.2014.
Texto completoRobert Nourgaliev, Nam Dinh y Robert Youngblood. Development, Selection, Implementation and Testing of Architectural Features and Solution Techniques for Next Generation of System Simulation Codes to Support the Safety Case if the LWR Life Extension. Office of Scientific and Technical Information (OSTI), diciembre de 2010. http://dx.doi.org/10.2172/1004227.
Texto completoLylo, Taras. Російсько-українська війна в інтерпретаціях іранського видання «The Tehran Times»: основні ідеологеми та маніпулятивні прийоми. Ivan Franko National University of Lviv, marzo de 2023. http://dx.doi.org/10.30970/vjo.2023.52-53.11730.
Texto completoRiccardella, Scott. PR-335-143705-R01 Study on Reliability of In-ditch NDE for SCC Anomalies. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), octubre de 2018. http://dx.doi.org/10.55274/r0011529.
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