Journal articles: 'LASSO algorithm'

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Kaneda, Yasuaki, and Yasuharu Irizuki. "Recursive Algorithm for LASSO." IEEJ Transactions on Electronics, Information and Systems 136, no. 7 (2016): 915–22. http://dx.doi.org/10.1541/ieejeiss.136.915.

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Jain, Rahi, and Wei Xu. "HDSI: High dimensional selection with interactions algorithm on feature selection and testing." PLOS ONE 16, no. 2 (February 16, 2021): e0246159. http://dx.doi.org/10.1371/journal.pone.0246159.

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Feature selection on high dimensional data along with the interaction effects is a critical challenge for classical statistical learning techniques. Existing feature selection algorithms such as random LASSO leverages LASSO capability to handle high dimensional data. However, the technique has two main limitations, namely the inability to consider interaction terms and the lack of a statistical test for determining the significance of selected features. This study proposes a High Dimensional Selection with Interactions (HDSI) algorithm, a new feature selection method, which can handle high-dimensional data, incorporate interaction terms, provide the statistical inferences of selected features and leverage the capability of existing classical statistical techniques. The method allows the application of any statistical technique like LASSO and subset selection on multiple bootstrapped samples; each contains randomly selected features. Each bootstrap data incorporates interaction terms for the randomly sampled features. The selected features from each model are pooled and their statistical significance is determined. The selected statistically significant features are used as the final output of the approach, whose final coefficients are estimated using appropriate statistical techniques. The performance of HDSI is evaluated using both simulated data and real studies. In general, HDSI outperforms the commonly used algorithms such as LASSO, subset selection, adaptive LASSO, random LASSO and group LASSO.

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Yau, Chun Yip, and Tsz Shing Hui. "LARS-type algorithm for group lasso." Statistics and Computing 27, no. 4 (May 23, 2016): 1041–48. http://dx.doi.org/10.1007/s11222-016-9669-7.

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Alghamdi, Maryam A., Mohammad Ali Alghamdi, Naseer Shahzad, and Hong-Kun Xu. "Properties and Iterative Methods for theQ-Lasso." Abstract and Applied Analysis 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/250943.

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We introduce theQ-lasso which generalizes the well-known lasso of Tibshirani (1996) withQa closed convex subset of a Euclideanm-space for some integerm≥1. This setQcan be interpreted as the set of errors within given tolerance level when linear measurements are taken to recover a signal/image via the lasso. Solutions of theQ-lasso depend on a tuning parameterγ. In this paper, we obtain basic properties of the solutions as a function ofγ. Because of ill posedness, we also applyl1-l2regularization to theQ-lasso. In addition, we discuss iterative methods for solving theQ-lasso which include the proximal-gradient algorithm and the projection-gradient algorithm.

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Wang, Jin-Jia, and Yang Lu. "Coordinate Descent Based Hierarchical Interactive Lasso Penalized Logistic Regression and Its Application to Classification Problems." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/430201.

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We present the hierarchical interactive lasso penalized logistic regression using the coordinate descent algorithm based on the hierarchy theory and variables interactions. We define the interaction model based on the geometric algebra and hierarchical constraint conditions and then use the coordinate descent algorithm to solve for the coefficients of the hierarchical interactive lasso model. We provide the results of some experiments based on UCI datasets, Madelon datasets from NIPS2003, and daily activities of the elder. The experimental results show that the variable interactions and hierarchy contribute significantly to the classification. The hierarchical interactive lasso has the advantages of the lasso and interactive lasso.

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Liu, Yashu, Jie Wang, and Jieping Ye. "An Efficient Algorithm For Weak Hierarchical Lasso." ACM Transactions on Knowledge Discovery from Data 10, no. 3 (February 24, 2016): 1–24. http://dx.doi.org/10.1145/2791295.

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Kim, Jinseog, Yuwon Kim, and Yongdai Kim. "A Gradient-Based Optimization Algorithm for LASSO." Journal of Computational and Graphical Statistics 17, no. 4 (December 2008): 994–1009. http://dx.doi.org/10.1198/106186008x386210.

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Wang, Hao. "Coordinate descent algorithm for covariance graphical lasso." Statistics and Computing 24, no. 4 (February 23, 2013): 521–29. http://dx.doi.org/10.1007/s11222-013-9385-5.

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Li, Yahui, Yang Li, and Yuanyuan Sun. "Online Static Security Assessment of Power Systems Based on Lasso Algorithm." Applied Sciences 8, no. 9 (August 23, 2018): 1442. http://dx.doi.org/10.3390/app8091442.

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As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is proposed in this paper to apply to online static security assessment (OSSA). The assessment is based on a security index, which is applied to select and screen contingencies. Firstly, the multi-step adaptive Lasso (MSA-Lasso) regression algorithm is introduced based on the regression algorithm, whose predictive performance has an advantage. Then, an OSSA module is proposed to evaluate and select contingencies in different load conditions. In addition, the Lasso algorithm is employed to predict the security index of each power system operation state with the consideration of bus voltages and power flows, according to Newton–Raphson load flow (NRLF) analysis in post-contingency states. Finally, the numerical results of applying the proposed approach to the IEEE 14-bus, 118-bus, and 300-bus test systems demonstrate the accuracy and rapidity of OSSA.

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Keerthi, S. S., and S. Shevade. "A Fast Tracking Algorithm for Generalized LARS/LASSO." IEEE Transactions on Neural Networks 18, no. 6 (November 2007): 1826–30. http://dx.doi.org/10.1109/tnn.2007.900229.

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Coelho, Frederico, Marcelo Costa, Michel Verleysen, and Antônio P. Braga. "LASSO multi-objective learning algorithm for feature selection." Soft Computing 24, no. 17 (February 3, 2020): 13209–17. http://dx.doi.org/10.1007/s00500-020-04734-w.

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Gao, Lu, Karsten Schulz, and Matthias Bernhardt. "Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO Algorithm." Advances in Meteorology 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/472741.

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Precipitation is an essential input parameter for land surface models because it controls a large variety of environmental processes. However, the commonly sparse meteorological networks in complex terrains are unable to provide the information needed for many applications. Therefore, downscaling local precipitation is necessary. To this end, a new machine learning method, LASSO algorithm (least absolute shrinkage and selection operator), is used to address the disparity between ERA-Interim forecast precipitation data (0.25° grid) and point-scale meteorological observations. LASSO was tested and validated against other three downscaling methods, local intensity scaling (LOCI), quantile-mapping (QM), and stepwise regression (Stepwise) at 50 meteorological stations, located in the high mountainous region of the central Alps. The downscaling procedure is implemented in two steps. Firstly, the dry or wet days are classified and the precipitation amounts conditional on the occurrence of wet days are modeled subsequently. Compared to other three downscaling methods, LASSO shows the best performances in precipitation occurrence and precipitation amount prediction on average. Furthermore, LASSO could reduce the error for certain sites, where no improvement could be seen when LOCI and QM were used. This study proves that LASSO is a reasonable alternative to other statistical methods with respect to the downscaling of precipitation data.

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Gao, Junbin, Paul W. Kwan, and Daming Shi. "Sparse kernel learning with LASSO and Bayesian inference algorithm." Neural Networks 23, no. 2 (March 2010): 257–64. http://dx.doi.org/10.1016/j.neunet.2009.07.001.

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Arnold, Taylor B., and Ryan J. Tibshirani. "Efficient Implementations of the Generalized Lasso Dual Path Algorithm." Journal of Computational and Graphical Statistics 25, no. 1 (January 2, 2016): 1–27. http://dx.doi.org/10.1080/10618600.2015.1008638.

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Hoefling, Holger. "A Path Algorithm for the Fused Lasso Signal Approximator." Journal of Computational and Graphical Statistics 19, no. 4 (January 2010): 984–1006. http://dx.doi.org/10.1198/jcgs.2010.09208.

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Hofleitner, A., T. Rabbani, L. El Ghaoui, and A. Bayen. "Online Homotopy Algorithm for a Generalization of the LASSO." IEEE Transactions on Automatic Control 58, no. 12 (December 2013): 3175–79. http://dx.doi.org/10.1109/tac.2013.2259373.

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Yuan, J., and G. Wei. "An efficient Monte Carlo EM algorithm for Bayesian lasso." Journal of Statistical Computation and Simulation 84, no. 10 (April 8, 2013): 2166–86. http://dx.doi.org/10.1080/00949655.2013.786080.

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Shi, Yueyong, Jian Huang, Yuling Jiao, Yicheng Kang, and Hu Zhang. "Generalized Newton–Raphson algorithm for high dimensional LASSO regression." Statistics and Its Interface 14, no. 3 (2021): 339–50. http://dx.doi.org/10.4310/20-sii643.

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Savin, Ivan. "A Comparative Study of the Lasso-type and Heuristic Model Selection Methods." Jahrbücher für Nationalökonomie und Statistik 233, no. 4 (August 1, 2013): 526–49. http://dx.doi.org/10.1515/jbnst-2013-0406.

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Summary This study presents a first comparative analysis of Lasso-type (Lasso, adaptive Lasso, elastic net) and heuristic subset selection methods. Although the Lasso has shown success in many situations, it has some limitations. In particular, inconsistent results are obtained for pairwise highly correlated predictors. An alternative to the Lasso is constituted by model selection based on information criteria (IC), which remain consistent in the situation mentioned. However, these criteria are hard to optimize due to a discrete search space. To overcome this problem, an optimization heuristic (Genetic Algorithm) is applied. To this end, results of a Monte-Carlo simulation study together with an application to an actual empirical problem are reported to illustrate the performance of the methods.

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Zhao, Yingdong, and Richard Simon. "Development and Validation of Predictive Indices for a Continuous Outcome Using Gene Expression Profiles." Cancer Informatics 9 (January 2010): CIN.S3805. http://dx.doi.org/10.4137/cin.s3805.

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There have been relatively few publications using linear regression models to predict a continuous response based on microarray expression profiles. Standard linear regression methods are problematic when the number of predictor variables exceeds the number of cases. We have evaluated three linear regression algorithms that can be used for the prediction of a continuous response based on high dimensional gene expression data. The three algorithms are the least angle regression (LAR), the least absolute shrinkage and selection operator (LASSO), and the averaged linear regression method (ALM). All methods are tested using simulations based on a real gene expression dataset and analyses of two sets of real gene expression data and using an unbiased complete cross validation approach. Our results show that the LASSO algorithm often provides a model with somewhat lower prediction error than the LAR method, but both of them perform more efficiently than the ALM predictor. We have developed a plug-in for BRB-ArrayTools that implements the LAR and the LASSO algorithms with complete cross-validation.

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Wang, Xu, Zi-Yu Li, and Jia-Yu Zhong. "Construction of Quantitative Transaction Strategy Based on LASSO and Neural Network." Applied Economics and Finance 4, no. 4 (June 21, 2017): 134. http://dx.doi.org/10.11114/aef.v4i4.2370.

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Since the establishment of the securities market, there has been a continuous search for the prediction of stock price trend. Based on the forecasting characteristics of stock index futures, this paper combines the variable selection in the statistical field and the machine learning to construct an effective quantitative trading strategy. Firstly, the LASSO algorithm is used to filter a large number of technical indexes to obtain reasonable and effective technical indicators. Then, the indicators are used as input variables, and the average expected return rate is predicted by neural network. Finally, based on the forecasting results, a reasonable quantitative trading strategy is constructed. We take the CSI 300 stock index futures trading data for empirical research. The results show that the variables selected by LASSO are effective and the introduction of LASSO can improve the generalization ability of neural network. At the same time, the quantitative trading strategy based on LASSO algorithm and neural network can achieve good effect and robustness at different times.

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Gillies, Christopher E., Xiaoli Gao, Nilesh V. Patel, Mohammad-Reza Siadat, and George D. Wilson. "Improved Feature Selection by Incorporating Gene Similarity into the LASSO." International Journal of Knowledge Discovery in Bioinformatics 3, no. 1 (January 2012): 1–22. http://dx.doi.org/10.4018/jkdb.2012010101.

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Personalized medicine is customizing treatments to a patient’s genetic profile and has the potential to revolutionize medical practice. An important process used in personalized medicine is gene expression profiling. Analyzing gene expression profiles is difficult, because there are usually few patients and thousands of genes, leading to the curse of dimensionality. To combat this problem, researchers suggest using prior knowledge to enhance feature selection for supervised learning algorithms. The authors propose an enhancement to the LASSO, a shrinkage and selection technique that induces parameter sparsity by penalizing a model’s objective function. Their enhancement gives preference to the selection of genes that are involved in similar biological processes. The authors’ modified LASSO selects similar genes by penalizing interaction terms between genes. They devise a coordinate descent algorithm to minimize the corresponding objective function. To evaluate their method, the authors created simulation data where they compared their model to the standard LASSO model and an interaction LASSO model. The authors’ model outperformed both the standard and interaction LASSO models in terms of detecting important genes and gene interactions for a reasonable number of training samples. They also demonstrated the performance of their method on a real gene expression data set from lung cancer cell lines.

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ChunRong, Chen, Chen ShanXiong, Chen Lin, and Zhu YuChen. "Method for Solving LASSO Problem Based on Multidimensional Weight." Advances in Artificial Intelligence 2017 (May 4, 2017): 1–9. http://dx.doi.org/10.1155/2017/1736389.

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In the data mining, the analysis of high-dimensional data is a critical but thorny research topic. The LASSO (least absolute shrinkage and selection operator) algorithm avoids the limitations, which generally employ stepwise regression with information criteria to choose the optimal model, existing in traditional methods. The improved-LARS (Least Angle Regression) algorithm solves the LASSO effectively. This paper presents an improved-LARS algorithm, which is constructed on the basis of multidimensional weight and intends to solve the problems in LASSO. Specifically, in order to distinguish the impact of each variable in the regression, we have separately introduced part of principal component analysis (Part_PCA), Independent Weight evaluation, and CRITIC, into our proposal. We have explored that these methods supported by our proposal change the regression track by weighted every individual, to optimize the approach direction, as well as the approach variable selection. As a consequence, our proposed algorithm can yield better results in the promise direction. Furthermore, we have illustrated the excellent property of LARS algorithm based on multidimensional weight by the Pima Indians Diabetes. The experiment results show an attractive performance improvement resulting from the proposed method, compared with the improved-LARS, when they are subjected to the same threshold value.

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Ajeel, Sherzad M., and Hussein A. Hashem. "Comparison Some Robust Regularization Methods in Linear Regression via Simulation Study." Academic Journal of Nawroz University 9, no. 2 (August 20, 2020): 244. http://dx.doi.org/10.25007/ajnu.v9n2a818.

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In this paper, we reviewed some variable selection methods in linear regression model. Conventional methodologies such as the Ordinary Least Squares (OLS) technique is one of the most commonly used method in estimating the parameters in linear regression. But the OLS estimates performs poorly when the dataset suffer from outliers or when the assumption of normality is violated such as in the case of heavy-tailed errors. To address this problem, robust regularized regression methods like Huber Lasso (Rosset and Zhu, 2007) and quantile regression (Koenker and Bassett ,1978] were proposed. This paper focuses on comparing the performance of the seven methods, the quantile regression estimates, the Huber Lasso estimates, the adaptive Huber Lasso estimates, the adaptive LAD Lasso, the Gamma-divergence estimates, the Maximum Tangent Likelihood Lasso (MTE) estimates and Semismooth Newton Coordinate Descent Algorithm (SNCD ) Huber loss estimates.

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Li, Hongtao, Chaoyu Wang, and Xiaohua Zhu. "Compressive Sensing for High-Resolution Direction-of-Arrival Estimation via Iterative Optimization on Sensing Matrix." International Journal of Antennas and Propagation 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/713930.

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A novel compressive sensing- (CS-) based direction-of-arrival (DOA) estimation algorithm is proposed to solve the performance degradation of the CS-based DOA estimation in the presence of sensing matrix mismatching. Firstly, a DOA sparse sensing model is set up in the presence of sensing matrix mismatching. Secondly, combining the Dantzig selector (DS) algorithm and least-absolute shrinkage and selection operator (LASSO) algorithm, a CS-based DOA estimation algorithm which performs iterative optimization alternatively on target angle information vector and sensing matrix mismatching error vector is proposed. The simulation result indicates that the proposed algorithm possesses higher angle resolution and estimation accuracy compared with conventional CS-based DOA estimation algorithms.

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Cho, Seo-Eun, Zong Woo Geem, and Kyoung-Sae Na. "Predicting Depression in Community Dwellers Using a Machine Learning Algorithm." Diagnostics 11, no. 8 (August 7, 2021): 1429. http://dx.doi.org/10.3390/diagnostics11081429.

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Depression is one of the leading causes of disability worldwide. Given the socioeconomic burden of depression, appropriate depression screening for community dwellers is necessary. We used data from the 2014 and 2016 Korea National Health and Nutrition Examination Surveys. The 2014 dataset was used as a training set, whereas the 2016 dataset was used as the hold-out test set. The synthetic minority oversampling technique (SMOTE) was used to control for class imbalances between the depression and non-depression groups in the 2014 dataset. The least absolute shrinkage and selection operator (LASSO) was used for feature reduction and classifiers in the final model. Data obtained from 9488 participants were used for the machine learning process. The depression group had poorer socioeconomic, health, functional, and biological measures than the non-depression group. From the initial 37 variables, 13 were selected using LASSO. All performance measures were calculated based on the raw 2016 dataset without the SMOTE. The area under the receiver operating characteristic curve and overall accuracy in the hold-out test set were 0.903 and 0.828, respectively. Perceived stress had the strongest influence on the classifying model for depression. LASSO can be practically applied for depression screening of community dwellers with a few variables. Future studies are needed to develop a more efficient and accurate classification model for depression.

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Klein, Barbara, Ronald Klein, Kristine Lee, Weiliang Shi, Grace Wahba, and Stephen Wright. "LASSO-Patternsearch algorithm with application to ophthalmology and genomic data." Statistics and Its Interface 1, no. 1 (2008): 137–53. http://dx.doi.org/10.4310/sii.2008.v1.n1.a12.

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Fujiwara, Yasuhiro, Yasutoshi Ida, Junya Arai, Mai Nishimura, and Sotetsu Iwamura. "Fast algorithm for the lasso based L 1 -graph construction." Proceedings of the VLDB Endowment 10, no. 3 (November 2016): 229–40. http://dx.doi.org/10.14778/3021924.3021938.

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Ahmed, Ismaïl, Antoine Pariente, and Pascale Tubert-Bitter. "Class-imbalanced subsampling lasso algorithm for discovering adverse drug reactions." Statistical Methods in Medical Research 27, no. 3 (April 25, 2016): 785–97. http://dx.doi.org/10.1177/0962280216643116.

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Background All methods routinely used to generate safety signals from pharmacovigilance databases rely on disproportionality analyses of counts aggregating patients’ spontaneous reports. Recently, it was proposed to analyze individual spontaneous reports directly using Bayesian lasso logistic regressions. Nevertheless, this raises the issue of choosing an adequate regularization parameter in a variable selection framework while accounting for computational constraints due to the high dimension of the data. Purpose Our main objective is to propose a method, which exploits the subsampling idea from Stability Selection, a variable selection procedure combining subsampling with a high-dimensional selection algorithm, and adapts it to the specificities of the spontaneous reporting data, the latter being characterized by their large size, their binary nature and their sparsity. Materials and method Given the large imbalance existing between the presence and absence of a given adverse event, we propose an alternative subsampling scheme to that of Stability Selection resulting in an over-representation of the minority class and a drastic reduction in the number of observations in each subsample. Simulations are used to help define the detection threshold as regards the average proportion of false signals. They are also used to compare the performances of the proposed sampling scheme with that originally proposed for Stability Selection. Finally, we compare the proposed method to the gamma Poisson shrinker, a disproportionality method, and to a lasso logistic regression approach through an empirical study conducted on the French national pharmacovigilance database and two sets of reference signals. Results Simulations show that the proposed sampling strategy performs better in terms of false discoveries and is faster than the equiprobable sampling of Stability Selection. The empirical evaluation illustrates the better performances of the proposed method compared with gamma Poisson shrinker and the lasso in terms of number of reference signals retrieved.

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Johnson, Nicholas A. "A Dynamic Programming Algorithm for the Fused Lasso andL0-Segmentation." Journal of Computational and Graphical Statistics 22, no. 2 (April 2013): 246–60. http://dx.doi.org/10.1080/10618600.2012.681238.

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Wilms, I., and C. Croux. "An algorithm for the multivariate group lasso with covariance estimation." Journal of Applied Statistics 45, no. 4 (February 13, 2017): 668–81. http://dx.doi.org/10.1080/02664763.2017.1289503.

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Maksimov, Mikhail O., István Pelczer, and A. James Link. "Precursor-centric genome-mining approach for lasso peptide discovery." Proceedings of the National Academy of Sciences 109, no. 38 (September 4, 2012): 15223–28. http://dx.doi.org/10.1073/pnas.1208978109.

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Lasso peptides are a class of ribosomally synthesized posttranslationally modified natural products found in bacteria. Currently known lasso peptides have a diverse set of pharmacologically relevant activities, including inhibition of bacterial growth, receptor antagonism, and enzyme inhibition. The biosynthesis of lasso peptides is specified by a cluster of three genes encoding a precursor protein and two enzymes. Here we develop a unique genome-mining algorithm to identify lasso peptide gene clusters in prokaryotes. Our approach involves pattern matching to a small number of conserved amino acids in precursor proteins, and thus allows for a more global survey of lasso peptide gene clusters than does homology-based genome mining. Of more than 3,000 currently sequenced prokaryotic genomes, we found 76 organisms that are putative lasso peptide producers. These organisms span nine bacterial phyla and an archaeal phylum. To provide validation of the genome-mining method, we focused on a single lasso peptide predicted to be produced by the freshwater bacterium Asticcacaulis excentricus. Heterologous expression of an engineered, minimal gene cluster in Escherichia coli led to the production of a unique lasso peptide, astexin-1. At 23 aa, astexin-1 is the largest lasso peptide isolated to date. It is also highly polar, in contrast to many lasso peptides that are primarily hydrophobic. Astexin-1 has modest antimicrobial activity against its phylogenetic relative Caulobacter crescentus. The solution structure of astexin-1 was determined revealing a unique topology that is stabilized by hydrogen bonding between segments of the peptide.

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Li, Zhongyu, Ka Ho Tsang, and Hoi Ying Wong. "Lasso-based simulation for high-dimensional multi-period portfolio optimization." IMA Journal of Management Mathematics 31, no. 3 (October 4, 2019): 257–80. http://dx.doi.org/10.1093/imaman/dpz013.

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Abstract This paper proposes a regression-based simulation algorithm for multi-period mean-variance portfolio optimization problems with constraints under a high-dimensional setting. For a high-dimensional portfolio, the least squares Monte Carlo algorithm for portfolio optimization can perform less satisfactorily with finite sample paths due to the estimation error from the ordinary least squares (OLS) in the regression steps. Our algorithm, which resolves this problem e, that demonstrates significant improvements in numerical performance for the case of finite sample path and high dimensionality. Specifically, we replace the OLS by the least absolute shrinkage and selection operator (lasso). Our major contribution is the proof of the asymptotic convergence of the novel lasso-based simulation in a recursive regression setting. Numerical experiments suggest that our algorithm achieves good stability in both low- and higher-dimensional cases.

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JING, LIPING, MICHAEL K. NG, and TIEYONG ZENG. "ON GENE SELECTION AND CLASSIFICATION FOR CANCER MICROARRAY DATA USING MULTI-STEP CLUSTERING AND SPARSE REPRESENTATION." Advances in Adaptive Data Analysis 03, no. 01n02 (April 2011): 127–48. http://dx.doi.org/10.1142/s1793536911000763.

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Microarray data profiles gene expression on a whole genome scale, and provides a good way to study associations between gene expression and occurrence or progression of cancer disease. Many researchers realized that microarray data is useful to predict cancer cases. However, the high dimension of gene expressions, which is significantly larger than the sample size, makes this task very difficult. It is very important to identify the significant genes causing cancer. Many feature selection algorithms have been proposed focusing on improving cancer predictive accuracy at the expense of ignoring the correlations between the features. In this work, a novel framework (named by SGS) is presented for significant genes selection and efficient cancer case classification. The proposed framework first performs a clustering algorithm to find the gene groups where genes in each group have higher correlation coefficient, and then selects (1) the significant (2) genes in each group using the Bayesian Lasso method and important gene groups using the group Lasso method, and finally builds a prediction model based on the shrinkage gene space with efficient classification algorithm (such as support vector machine (SVM), 1NN, and regression). Experimental results on public available microarray data show that the proposed framework often outperforms the existing feature selection and prediction methods such as SAM, information gain (IG), and Lasso-type prediction models.

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Shi, Yuanyuan, Junyu Zhao, Xianchong Song, Zuoyu Qin, Lichao Wu, Huili Wang, and Jian Tang. "Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm." PLOS ONE 16, no. 6 (June 28, 2021): e0253385. http://dx.doi.org/10.1371/journal.pone.0253385.

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Effective soil spectral band selection and modeling methods can improve modeling accuracy. To establish a hyperspectral prediction model of soil organic matter (SOM) content, this study investigated a forested Eucalyptus plantation in Huangmian Forest Farm, Guangxi, China. The Ranger and Lasso algorithms were used to screen spectral bands. Subsequently, models were established using four algorithms: partial least squares regression, random forest (RF), a support vector machine, and an artificial neural network (ANN). The optimal model was then selected. The results showed that the modeling accuracy was higher when band selection was based on the Ranger algorithm than when it was based on the Lasso algorithm. ANN modeling had the best goodness of fit, and the model established by RF had the most stable modeling results. Based on the above results, a new method is proposed in this study for band selection in the early phase of soil hyperspectral modeling. The Ranger algorithm can be applied to screen the spectral bands, and ANN or RF can then be selected to construct the prediction model based on different datasets, which is applicable to establish the prediction model of SOM content in red soil plantations. This study provides a reference for the remote sensing of soil fertility in forests of different soil types and a theoretical basis for developing portable equipment for the hyperspectral measurement of SOM content in forest habitats.

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Guo, Hongping, Zuguo Yu, Jiyuan An, Guosheng Han, Yuanlin Ma, and Runbin Tang. "A Two-Stage Mutual Information Based Bayesian Lasso Algorithm for Multi-Locus Genome-Wide Association Studies." Entropy 22, no. 3 (March 13, 2020): 329. http://dx.doi.org/10.3390/e22030329.

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Genome-wide association study (GWAS) has turned out to be an essential technology for exploring the genetic mechanism of complex traits. To reduce the complexity of computation, it is well accepted to remove unrelated single nucleotide polymorphisms (SNPs) before GWAS, e.g., by using iterative sure independence screening expectation-maximization Bayesian Lasso (ISIS EM-BLASSO) method. In this work, a modified version of ISIS EM-BLASSO is proposed, which reduces the number of SNPs by a screening methodology based on Pearson correlation and mutual information, then estimates the effects via EM-Bayesian Lasso (EM-BLASSO), and finally detects the true quantitative trait nucleotides (QTNs) through likelihood ratio test. We call our method a two-stage mutual information based Bayesian Lasso (MBLASSO). Under three simulation scenarios, MBLASSO improves the statistical power and retains the higher effect estimation accuracy when comparing with three other algorithms. Moreover, MBLASSO performs best on model fitting, the accuracy of detected associations is the highest, and 21 genes can only be detected by MBLASSO in Arabidopsis thaliana datasets.

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Sa'adah, Umu, Masithoh Yessi Rochayani, and Ani Budi Astuti. "Knowledge discovery from gene expression dataset using bagging lasso decision tree." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (February 1, 2021): 1151. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp1151-1159.

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<p>Classifying high-dimensional data are a challenging task in data mining. Gene expression data is a type of high-dimensional data that has thousands of features. The study was proposing a method to extract knowledge from high-dimensional gene expression data by selecting features and classifying. Lasso was used for selecting features and the classification and regression tree (CART) algorithm was used to construct the decision tree model. To examine the stability of the lasso decision tree, we performed bootstrap aggregating (Bagging) with 50 replications. The gene expression data used was an ovarian tumor dataset that has 1,545 observations, 10,935 gene features, and binary class. The findings of this research showed that the lasso decision tree could produce an interpretable model that theoretically correct and had an accuracy of 89.32%. Meanwhile, the model obtained from the majority vote gave an accuracy of 90.29% which showed an increase in accuracy of 1% from the single lasso decision tree model. The slightly increasing accuracy shows that the lasso decision tree classifier is stable.</p>

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Gao, Fei, Bin Yan, Jian Chen, Minghui Wu, and Dapeng Shi. "Pathological grading of Hepatocellular Carcinomas in MRI using a LASSO algorithm." Journal of Physics: Conference Series 1053 (July 2018): 012095. http://dx.doi.org/10.1088/1742-6596/1053/1/012095.

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Zhu, Yunzhang. "An Augmented ADMM Algorithm With Application to the Generalized Lasso Problem." Journal of Computational and Graphical Statistics 26, no. 1 (January 2, 2017): 195–204. http://dx.doi.org/10.1080/10618600.2015.1114491.

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Yang, Yi, and Hui Zou. "A fast unified algorithm for solving group-lasso penalize learning problems." Statistics and Computing 25, no. 6 (August 4, 2014): 1129–41. http://dx.doi.org/10.1007/s11222-014-9498-5.

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De Vito, Ernesto, Veronica Umanità, and Silvia Villa. "A consistent algorithm to solve Lasso, elastic-net and Tikhonov regularization." Journal of Complexity 27, no. 2 (April 2011): 188–200. http://dx.doi.org/10.1016/j.jco.2011.01.003.

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Li, Xin, Peng Yi, Wei Wei, Yiming Jiang, and Le Tian. "LNNLS-KH: A Feature Selection Method for Network Intrusion Detection." Security and Communication Networks 2021 (January 6, 2021): 1–22. http://dx.doi.org/10.1155/2021/8830431.

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As an important part of intrusion detection, feature selection plays a significant role in improving the performance of intrusion detection. Krill herd (KH) algorithm is an efficient swarm intelligence algorithm with excellent performance in data mining. To solve the problem of low efficiency and high false positive rate in intrusion detection caused by increasing high-dimensional data, an improved krill swarm algorithm based on linear nearest neighbor lasso step (LNNLS-KH) is proposed for feature selection of network intrusion detection. The number of selected features and classification accuracy are introduced into fitness evaluation function of LNNLS-KH algorithm, and the physical diffusion motion of the krill individuals is transformed by a nonlinear method. Meanwhile, the linear nearest neighbor lasso step optimization is performed on the updated krill herd position in order to derive the global optimal solution. Experiments show that the LNNLS-KH algorithm retains 7 features in NSL-KDD dataset and 10.2 features in CICIDS2017 dataset on average, which effectively eliminates redundant features while ensuring high detection accuracy. Compared with the CMPSO, ACO, KH, and IKH algorithms, it reduces features by 44%, 42.86%, 34.88%, and 24.32% in NSL-KDD dataset, and 57.85%, 52.34%, 27.14%, and 25% in CICIDS2017 dataset, respectively. The classification accuracy increased by 10.03% and 5.39%, and the detection rate increased by 8.63% and 5.45%. Time of intrusion detection decreased by 12.41% and 4.03% on average. Furthermore, LNNLS-KH algorithm quickly jumps out of the local optimal solution and shows good performance in the optimal fitness iteration curve, convergence speed, and false positive rate of detection.

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Lei, J. "Fast exact conformalization of the lasso using piecewise linear homotopy." Biometrika 106, no. 4 (September 30, 2019): 749–64. http://dx.doi.org/10.1093/biomet/asz046.

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Summary Conformal prediction is a general method that converts almost any point predictor to a prediction set. The resulting set retains the good statistical properties of the original estimator under standard assumptions, and guarantees valid average coverage even when the model is mis-specified. A main challenge in applying conformal prediction in modern applications is efficient computation, as it generally requires an exhaustive search over the entire output space. In this paper we develop an exact and computationally efficient conformalization of the lasso and elastic net. The method makes use of a novel piecewise linear homotopy of the lasso solution under perturbation of a single input sample point. As a by-product, we provide a simpler and better-justified online lasso algorithm, which may be of independent interest. Our derivation also reveals an interesting accuracy-stability trade-off in conformal inference, which is analogous to the bias-variance trade-off in traditional parameter estimation. The practical performance of the new algorithm is demonstrated in both synthetic and real data examples.

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Zhou, Zhi Hui, Gui Xia Liu, Ling Tao Su, Liang Han, and Lun Yan. "Detecting Epistasis by LASSO-Penalized-Model Search Algorithm in Human Genome-Wide Association Studies." Advanced Materials Research 989-994 (July 2014): 2426–30. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2426.

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Extensive studies have shown that many complex diseases are influenced by interaction of certain genes, while due to the limitations and drawbacks of adopting logistic regression (LR) to detect epistasis in human Genome-Wide Association Studies (GWAS), we propose a new method named LASSO-penalized-model search algorithm (LPMA) by restricting it to a tuning constant and combining it with a penalization of the L1-norm of the complexity parameter, and it is implemented utilizing the idea of multi-step strategy. LASSO penalized regression particularly shows advantageous properties when the number of factors far exceeds the number of samples. We compare the performance of LPMA with its competitors. Through simulated data experiments, LPMA performs better regarding to the identification of epistasis and prediction accuracy.

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Fercoq, Olivier, and Zheng Qu. "Adaptive restart of accelerated gradient methods under local quadratic growth condition." IMA Journal of Numerical Analysis 39, no. 4 (March 5, 2019): 2069–95. http://dx.doi.org/10.1093/imanum/drz007.

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Abstract By analyzing accelerated proximal gradient methods under a local quadratic growth condition, we show that restarting these algorithms at any frequency gives a globally linearly convergent algorithm. This result was previously known only for long enough frequencies. Then as the rate of convergence depends on the match between the frequency and the quadratic error bound, we design a scheme to automatically adapt the frequency of restart from the observed decrease of the norm of the gradient mapping. Our algorithm has a better theoretical bound than previously proposed methods for the adaptation to the quadratic error bound of the objective. We illustrate the efficiency of the algorithm on Lasso, regularized logistic regression and total variation denoising problems.

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Cui Fangxiao, 崔方晓, 李大成 Li Dacheng, 吴军 Wujun, 王安静 Wang Anjing, and 李扬裕 Li Yangyu. "Adaptive Feature Extraction Algorithm Based on Lasso Method for Detecting Polluted Gas." Acta Optica Sinica 39, no. 5 (2019): 0530003. http://dx.doi.org/10.3788/aos201939.0530003.

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Li, Jingmin, Felix Pollinger, and Heiko Paeth. "Comparing the Lasso Predictor-Selection and Regression Method with Classical Approaches of Precipitation Bias Adjustment in Decadal Climate Predictions." Monthly Weather Review 148, no. 10 (October 1, 2020): 4339–51. http://dx.doi.org/10.1175/mwr-d-19-0302.1.

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AbstractIn this study, we investigate the technical application of the regularized regression method Lasso for identifying systematic biases in decadal precipitation predictions from a high-resolution regional climate model (CCLM) for Europe. The Lasso approach is quite novel in climatological research. We apply Lasso to observed precipitation and a large number of predictors related to precipitation derived from a training simulation, and transfer the trained Lasso regression model to a virtual forecast simulation for testing. Derived predictors from the model include local predictors at a given grid box and EOF predictors that describe large-scale patterns of variability for the same simulated variables. A major added value of the Lasso function is the variation of the so-called shrinkage factor and its ability in eliminating irrelevant predictors and avoiding overfitting. Among 18 different settings, an optimal shrinkage factor is identified that indicates a robust relationship between predictand and predictors. It turned out that large-scale patterns as represented by the EOF predictors outperform local predictors. The bias adjustment using the Lasso approach mainly improves the seasonal cycle of the precipitation prediction and, hence, improves the phase relationship and reduces the root-mean-square error between model prediction and observations. Another goal of the study pertains to the comparison of the Lasso performance with classical model output statistics and with a bivariate bias correction approach. In fact, Lasso is characterized by a similar and regionally higher skill than classical approaches of model bias correction. In addition, it is computationally less expensive. Therefore, we see a large potential for the application of the Lasso algorithm in a wider range of climatological applications when it comes to regression-based statistical transfer functions in statistical downscaling and model bias adjustment.

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Liu, Chao-Hong, Chin-Shiuh Shieh, Tai-Lin Huang, Chih-Hsueh Lin, Pei-Ju Chao, Yu-Jie Huang, Hsiao-Fei Lee, et al. "Evaluating the Risk Factors of Post Inflammatory Hyperpigmentation Complications with Nd-YAG Laser Toning Using LASSO-Based Algorithm." Applied Sciences 10, no. 6 (March 18, 2020): 2049. http://dx.doi.org/10.3390/app10062049.

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The neodymium-doped yttrium aluminum garnet (Nd-YAG) laser is used for removal of pigmented skin patches and rejuvenation of skin. However, complications such as hyperpigmentation, hypopigmentation, and petechiae can occur after frequent treatments. Therefore, identifying the risk factors for such complications is important. The development of a multivariable logistic regression model with least absolute shrinkage and selection operator (LASSO) is needed to provide valid predictions about the incidence of post inflammatory hyperpigmentation complication probability (PIHCP) among patients treated with Nd-YAG laser toning. A total of 125 female patients undergoing laser toning therapy between January 2014 and January 2016 were examined for post-inflammatory hyperpigmentation (PIH) complications. Factor analysis was performed using 15 potential predictive risk factors of PIH determined by a physician. The LASSO algorithm with cross-validation was used to select the optimal number of predictive risk factors from the potential factors for a multivariate logistic regression PIH complication model. The optimal number of predictive risk factors for the model was five: immediate endpoints of laser (IEL), α-hydroxy acid (AHA) peels, Fitzpatrick skin phototype (FSPT), acne, and melasma. The area under the receiver operating characteristic curve (AUC) was 0.79 (95% CI, 0.70–0.88) in the optimal model. The overall performance of the LASSO-based PIHCP model was satisfactory based on the AUC, Omnibus, Nagelkerke R2, and Hosmer–Lemeshow tests. This predictive risk factor model is useful to further optimize laser toning treatment related to PIH. The LASSO-based PIHCP model could be useful for decision-making.

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Luo, Mi, Yifu Wang, Yunhong Xie, Lai Zhou, Jingjing Qiao, Siyu Qiu, and Yujun Sun. "Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass." Forests 12, no. 2 (February 13, 2021): 216. http://dx.doi.org/10.3390/f12020216.

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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.

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Liu, Xiao-Ying, Yong Liang, Zong-Ben Xu, Hai Zhang, and Kwong-Sak Leung. "AdaptiveL1/2Shooting Regularization Method for Survival Analysis Using Gene Expression Data." Scientific World Journal 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/475702.

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A new adaptiveL1/2shooting regularization method for variable selection based on the Cox’s proportional hazards mode being proposed. This adaptiveL1/2shooting algorithm can be easily obtained by the optimization of a reweighed iterative series ofL1penalties and a shooting strategy ofL1/2penalty. Simulation results based on high dimensional artificial data show that the adaptiveL1/2shooting regularization method can be more accurate for variable selection than Lasso and adaptive Lasso methods. The results from real gene expression dataset (DLBCL) also indicate that theL1/2regularization method performs competitively.

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Journal articles on the topic 'LASSO algorithm'

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