Relevant bibliographies by topics / Backward selection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Backward selection'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Backward selection"

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Jaafar, W. Z. W., and D. Han. "Variable Selection Using the Gamma Test Forward and Backward Selections." Journal of Hydrologic Engineering 17, no. 1 (January 2012): 182–90. http://dx.doi.org/10.1061/(asce)he.1943-5584.0000403.

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Fitrianah, Devi, and Hisyam Fahmi. "THE IDENTIFICATION OF DETERMINANT PARAMETER IN FOREST FIRE BASED ON FEATURE SELECTION ALGORITHMS." SINERGI 23, no. 3 (October 11, 2019): 184. http://dx.doi.org/10.22441/sinergi.2019.3.002.

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This research conducts studies of the use of the Sequential Forward Floating Selection (SFFS) Algorithm and Sequential Backward Floating Selection (SBFS) Algorithm as the feature selection algorithms in the Forest Fire case study. With the supporting data that become the features of the forest fire case, we obtained information regarding the kinds of features that are very significant and influential in the event of a forest fire. Data used are weather data and land coverage of each area where the forest fire occurs. Based on the existing data, ten features were included in selecting the features using both feature selection methods. The result of the Sequential Forward Floating Selection method shows that earth surface temperature is the most significant and influential feature in regards to forest fire, while, based on the result of the Sequential Backward Feature Selection method, cloud coverage, is the most significant. Referring to the results from a total of 100 tests, the average accuracy of the Sequential Forward Floating Selection method is 96.23%. It surpassed the 82.41% average accuracy percentage of the Sequential Backward Floating Selection method.
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ESCARDÓ, MARTÍN, and PAULO OLIVA. "Selection functions, bar recursion and backward induction." Mathematical Structures in Computer Science 20, no. 2 (March 25, 2010): 127–68. http://dx.doi.org/10.1017/s0960129509990351.

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Bar recursion arises in constructive mathematics, logic, proof theory and higher-type computability theory. We explain bar recursion in terms of sequential games, and show how it can be naturally understood as a generalisation of the principle of backward induction that arises in game theory. In summary, bar recursion calculates optimal plays and optimal strategies, which, for particular games of interest, amount to equilibria. We consider finite games and continuous countably infinite games, and relate the two. The above development is followed by a conceptual explanation of how the finite version of the main form of bar recursion considered here arises from a strong monad of selections functions that can be defined in any cartesian closed category. Finite bar recursion turns out to be a well-known morphism available in any strong monad, specialised to the selection monad.
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Sapiecha, K., D. Banaszek, and R. Jarocki. "Backward error recovery with dynamic alternate selection." Microprocessing and Microprogramming 16, no. 4-5 (November 1985): 217–19. http://dx.doi.org/10.1016/0165-6074(85)90005-5.

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Evans, George W., and Bruce McGough. "Equilibrium selection, observability and backward-stable solutions." Journal of Monetary Economics 98 (October 2018): 1–10. http://dx.doi.org/10.1016/j.jmoneco.2018.04.004.

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Mao, K. Z. "Orthogonal Forward Selection and Backward Elimination Algorithms for Feature Subset Selection." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 1 (February 2004): 629–34. http://dx.doi.org/10.1109/tsmcb.2002.804363.

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Chotchantarakun, Knitchepon, and Ohm Sornil. "Adaptive Multi-level Backward Tracking for Sequential Feature Selection." Journal of ICT Research and Applications 15, no. 1 (June 29, 2021): 1–20. http://dx.doi.org/10.5614/itbj.ict.res.appl.2021.15.1.1.

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In the past few decades, the large amount of available data has become a major challenge in data mining and machine learning. Feature selection is a significant preprocessing step for selecting the most informative features by removing irrelevant and redundant features, especially for large datasets. These selected features play an important role in information searching and enhancing the performance of machine learning models. In this research, we propose a new technique called One-level Forward Multi-level Backward Selection (OFMB). The proposed algorithm consists of two phases. The first phase aims to create preliminarily selected subsets. The second phase provides an improvement on the previous result by an adaptive multi-level backward searching technique. Hence, the idea is to apply an improvement step during the feature addition and an adaptive search method on the backtracking step. We have tested our algorithm on twelve standard UCI datasets based on k-nearest neighbor and naive Bayes classifiers. Their accuracy was then compared with some popular methods. OFMB showed better results than the other sequential forward searching techniques for most of the tested datasets.
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Cotter, S. F., K. Kreutz-Delgado, and B. D. Rao. "Backward sequential elimination for sparse vector subset selection." Signal Processing 81, no. 9 (September 2001): 1849–64. http://dx.doi.org/10.1016/s0165-1684(01)00064-0.

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Armes, Jocelyn W. "Backward Design and Repertoire Selection: Finding Full Expression." Music Educators Journal 106, no. 3 (March 2020): 54–59. http://dx.doi.org/10.1177/0027432119893735.

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Repertoire selection is one of the most impactful responsibilities music educators undertake, and the music an educator chooses for students to experience reveals implicit and explicit philosophical values. When ensemble instructors select repertoire, their decisions are often informed by an eclectic mixture of competing practical and aspirational considerations. Backward design is an instructional strategy that aligns philosophical and instructional goals and allows music instructors to purposefully select high-quality repertoire and materials as means to an end rather than an end in themselves.
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Giselsson, Pontus, and Stephen Boyd. "Metric selection in fast dual forward–backward splitting." Automatica 62 (December 2015): 1–10. http://dx.doi.org/10.1016/j.automatica.2015.09.010.

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Dissertations / Theses on the topic "Backward selection"

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Li, Xin. "A simulation evaluation of backward elimination and stepwise variable selection in regression analysis." Kansas State University, 2012. http://hdl.handle.net/2097/14094.

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Master of Science
Department of Statistics
Paul Nelson
A first step in model building in regression analysis often consists of selecting a parsimonious set of independent variables from a pool of candidate independent variables. This report uses simulation to study and compare the performance of two widely used sequential, variable selection algorithms, stepwise and backward elimination. A score is developed to assess the ability of any variable selection method to terminate with the correct model. It is found that backward elimination performs slightly better than stepwise, increasing sample size leads to a relatively small improvement in both methods and that the magnitude of the variance of the error term is the major factor determining the performance of both.
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SINGH, KEVIN. "Comparing Variable Selection Algorithms On Logistic Regression – A Simulation." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446090.

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When we try to understand why some schools perform worse than others, if Covid-19 has struck harder on some demographics or whether income correlates with increased happiness, we may turn to regression to better understand how these variables are correlated. To capture the true relationship between variables we may use variable selection methods in order to ensure that the variables which have an actual effect have been included in the model. Choosing the right model for variable selection is vital. Without it there is a risk of including variables which have little to do with the dependent variable or excluding variables that are important. Failing to capture the true effects would paint a picture disconnected from reality and it would also give a false impression of what reality really looks like. To mitigate this risk a simulation study has been conducted to find out what variable selection algorithms to apply in order to make more accurate inference. The different algorithms being tested are stepwise regression, backward elimination and lasso regression. Lasso performed worst when applied to a small sample but performed best when applied to larger samples. Backward elimination and stepwise regression had very similar results.
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Tummala, Suprajaa. "Heuristics for Signal Selection in Post-Silicon Validation." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1573573253403988.

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Stevenson, Clint W. "A Logistic Regression Analysis of Utah Colleges Exit Poll Response Rates Using SAS Software." BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/1116.

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In this study I examine voter response at an interview level using a dataset of 7562 voter contacts (including responses and nonresponses) in the 2004 Utah Colleges Exit Poll. In 2004, 4908 of the 7562 voters approached responded to the exit poll for an overall response rate of 65 percent. Logistic regression is used to estimate factors that contribute to a success or failure of each interview attempt. This logistic regression model uses interviewer characteristics, voter characteristics (both respondents and nonrespondents), and exogenous factors as independent variables. Voter characteristics such as race, gender, and age are strongly associated with response. An interviewer's prior retail sales experience is associated with whether a voter will decide to respond to a questionnaire or not. The only exogenous factor that is associated with voter response is whether the interview occurred in the morning or afternoon.
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"Portfolio selection using both forward- and backward- looking information." 2014. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1291382.

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Shen, Jianguo.
Thesis M.Phil. Chinese University of Hong Kong 2014.
Includes bibliographical references (leaves 48-51).
Abstracts also in Chinese.
Title from PDF title page (viewed on 27, September, 2016).
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Huang, Chien-Hsun, and 黃建勳. "Using Backward-type Portfolio Selection Methods to Construct Optimal Portfolio Evaluated Index and Model." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/74797563727817301130.

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碩士
國立清華大學
工業工程與工程管理學系
92
Portfolio selection methods are developed in many fields. Many techniques and mathematical models are used to settle related problems based on mean-variance model developed in the stock markets. Many researches focus on evaluating items and formulate portfolio from good items and the methods belong to forward-type. On the contrary, this study aims to use “backward-type” portfolio selection method. In the perspective of backward-type selection, this thesis identifies the portfolio attributes into three categories such as independent, interrelated and synergistic portfolio attributes. Other than the mean-variance model considers the risk as the selected criteria. The thesis used the performance (i.e. future return) what the investor emphasized as the target. By the statistic of partial R squares from stepwise-regression method toward performance, the investors’ attitude (i.e. relative importance) of each attribute is obtained periodically and the evaluation index is constructed. Based on the index, the study then constructed multi-criteria mixed-integer quadratic programming model and quadratic programming by different definition of synergistic attributes to obtain invested position of stocks in the portfolio. Finally, This study will have illustrations in Taiwan Stock market and find that the backward-type selection methods, company profitability and synergistic attribute including in the model will have good performance.
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Liu, Ching-Fang, and 劉靜方. "Developing Backward-type Hyper-portfolio Selection Framework to Evaluate Multi-layer Portfolios with Interdependent Items." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/40193238083588650919.

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Abstract:
碩士
國立清華大學
工業工程與工程管理學系
93
Portfolio selection has been developed as a decision method for centuries and it is adopted in many fields for evaluating and selecting portfolios of multi-attribute item. Many studies have dealt this problem by decomposing complicated problems and focusing on single layer of portfolio to solve the subporblems. This study aims to develop a “hyper-portfolio” selection framework for the multi-layer portfolio decision problem. Multi-layer portfolio problem is defined as a combination of portfolios which are the combinations of items. In particular, Take, for example, menu consists of combination of interrelated meals which consist of combinations of interrelated food items. The proposed generic hyper-portfolio selection framework is based on backward-type selection. The vertical interactions between two layers of portfolio are derived with portfolio interactions between portfolio components. In hyper-portfolio, identified independent, interrelated and synergistic attributes perform similar properties in portfolio selection; in addition, interdependent attribute is proposed for inseparable affiliation of portfolio elements. This thesis applies the proposed framework for menu design problem with nine food item attributes during time horizon. Based on the item attributes, the portfolio selection is for meal design; furthermore, the hyper-portfolio selection evaluates menu performance by a multi-criteria mixed-integer-linear-programming (MILP) model. Finally, the thesis illustrates the hyper-portfolio MILP model calculating process, and a real nurse house menu design case. We find that hyper-portfolio selection outperforms in dealing with multi-attribute and hierarchical decision problems.
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Chen, Nai-Ching, and 陳乃慶. "TE01 Gyrotron Backward-Wave Oscillator with Mode Selective Circuit." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/70691579161214911386.

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Books on the topic "Backward selection"

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Singh, Shyama Nand. Reservation policy for backward classes. Jaipur: Rawat Publications, 1996.

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Did Darwin write the Origin backwards?: Philosophical essays on Darwin's theory. Amherst, NY: Prometheus Books, 2011.

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Book chapters on the topic "Backward selection"

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Cohen, Shimon, and Nathan Intrator. "Forward and Backward Selection in Regression Hybrid Network." In Multiple Classifier Systems, 98–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45428-4_10.

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Pang, Qing-Qing, and Li Zhang. "Fast Backward Iterative Laplacian Score for Unsupervised Feature Selection." In Knowledge Science, Engineering and Management, 409–20. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55130-8_36.

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Eşanu, Andreea. "A Backward Question About Multilevel Selection: Can Species Selection Help Disentangle the Notion of Group Selection?" In Multilevel Selection and the Theory of Evolution, 123–48. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78677-3_6.

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Xue, Yangtao, Li Zhang, and Bangjun Wang. "Dissimilarity-Based Sequential Backward Feature Selection Algorithm for Fault Diagnosis." In Neural Information Processing, 393–401. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70093-9_41.

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Bogdan, Karina Olga Maizman, and Valdinei Freire da Silva. "Forward and Backward Feature Selection in Gradient-Based MDP Algorithms." In Advances in Artificial Intelligence, 383–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37807-2_33.

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Szajowski, Krzysztof. "Optimal Choice Problem with Uncertainty of Selection and Backward Solicitation." In Transactions of the Tenth Prague Conference on Information Theory, Statistical Decision Functions, Random Processes, 357–66. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-010-9913-4_45.

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Poli, Riccardo. "Tournament Selection, Iterated Coupon-Collection Problem, and Backward-Chaining Evolutionary Algorithms." In Foundations of Genetic Algorithms, 132–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11513575_8.

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Ji, Tianqi, Jun Li, and Jianhua Xu. "Label Selection Algorithm Based on Boolean Interpolative Decomposition with Sequential Backward Selection for Multi-label Classification." In Document Analysis and Recognition – ICDAR 2021, 130–44. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86331-9_9.

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Ghosh, Soumen, P. S. V. S. Sai Prasad, and C. Raghavendra Rao. "Third Order Backward Elimination Approach for Fuzzy-Rough Set Based Feature Selection." In Lecture Notes in Computer Science, 254–62. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69900-4_32.

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Guillén, Alberto, Antti Sorjamaa, Gines Rubio, Amaury Lendasse, and Ignacio Rojas. "Mutual Information Based Initialization of Forward-Backward Search for Feature Selection in Regression Problems." In Artificial Neural Networks – ICANN 2009, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04274-4_1.

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Conference papers on the topic "Backward selection"

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"INTERLEAVING FORWARD BACKWARD FEATURE SELECTION." In International Conference on Knowledge Discovery and Information Retrieval. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003093204540457.

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Hu, Ye, Chen Changhua, Ning Hui, and Teng Yan. "Mode selection in overmoded relativistic backward-wave oscillator." In 2015 IEEE International Vacuum Electronics Conference (IVEC). IEEE, 2015. http://dx.doi.org/10.1109/ivec.2015.7223840.

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Ma, Mingyang, Shaohui Mei, Shuai Wan, Zhiyong Wang, and David Dagan Feng. "Forward-Backward Nonlinear Sparse Dictionary Selection Based Video Summarization." In 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM). IEEE, 2018. http://dx.doi.org/10.1109/bigmm.2018.8499074.

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Mustafa, Suleiman. "Feature selection using sequential backward method in melanoma recognition." In 2017 13th International Conference on Electronics, Computer and Computation (ICECCO). IEEE, 2017. http://dx.doi.org/10.1109/icecco.2017.8333341.

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Déjean, Sébastien, Radu Tudor Ionescu, Josiane Mothe, and Md Zia Ullah. "Forward and backward feature selection for query performance prediction." In SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3341105.3373904.

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Ramezani, Mohsen, Parham Moradi, and Fardin Akhlaghian Tab. "Improve performance of collaborative filtering systems using backward feature selection." In 2013 5th Conference on Information and Knowledge Technology (IKT). IEEE, 2013. http://dx.doi.org/10.1109/ikt.2013.6620069.

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El-sallam, A. A., S. Kayhan, and A. M. Zoubir. "Two backward elimination based approaches for low order model selection." In Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings. IEEE, 2003. http://dx.doi.org/10.1109/isspa.2003.1224932.

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Nagatani, Takashi, and Shigeo Abe. "Backward Varilable Selection of Support Vector Regressors by Block Deletion." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371285.

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Lee, Jung Hoon, Jin Lee, and Sin-Chong Park. "Backward compatible transmit diversity by antenna selection for IEEE 802.11a devices." In 2006 International Conference on Communication Technology. IEEE, 2006. http://dx.doi.org/10.1109/icct.2006.341874.

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Chen, Li, Miska M. Hannuksela, and Houqiang Li. "Adaptive block size selection for inter-layer backward view synthesis prediction." In SPIE/COS Photonics Asia, edited by Qionghai Dai and Tsutomu Shimura. SPIE, 2014. http://dx.doi.org/10.1117/12.2073286.

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