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Training data augmentation using generative models with statistical guarantees for materials informatics

SOFT COMPUTING 2022年第3期26卷 1181-1196页

作者： Ohno, Hiroshi Toyota Cent Res & Dev Labs Inc 41-1 Yokomichi Nagakute Aichi 4801192 Japan

In materials double dagger science, the small data size problem occurs frequently when applying machine learning algorithms. This issue is primarily due to, for example, real experimental measurement cost and time-consuming simulations based on materials models, e.g., density functional theory. We address the small data issue by generating training data using generative models. The proposed training data augmentation method can generate data using kernel density estimation models while maintaining statistical guarantees using unbiased estimators of linear regression, which has low computational costs and easy hyper-parameter tuning compared to deep neural networks. In addition, we derive an upper bound for the L-1 loss of the proposed method relative to probability density functions. Experiments were conducted with four benchmark and three materials (i.e., binary compounds, oxide ionic conductivity, and phosphorescent materials) datasets. Regarding the generated data, we examined training and generalization performances using kernel ridge regression compared to those of generative adversarial networks and real-NVPs. For the materials datasets, we analyzed influential factors regarding material properties (conductivity and emission intensity). The experimental results demonstrate that the proposed method can generate data that can be used as new training data.

关键词： linear regression model Density estimation Data augmentation Generative models Materials informatics

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Moderate-Dimensional Inferences on Quadratic Functionals in Ordinary Least Squares

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2022年第540期117卷 1931-1950页

作者： Guo, Xiao Cheng, Guang Univ Sci & Technol China Sch Management Int Inst Finance Hefei Anhui Peoples R China Purdue Univ Dept Stat W Lafayette IN 47906 USA

Statistical inferences for quadratic functionals of linear regression parameter have found wide applications including signal detection, global testing, inferences of error variance and fraction of variance explained. Classical theory based on ordinary least squares estimator works perfectly in the low-dimensional regime, but fails when the parameter dimension pn grows proportionally to the sample size n. In some cases, its performance is not satisfactory even when n >= 5p(n). The main contribution of this article is to develop dimension-adaptive inferences for quadratic functionals when lim(n ->infinity) p(n)/n = tau is an element of [0, 1). We propose a bias-and-variance-corrected test statistic and demonstrate that its theoretical validity (such as consistency and asymptotic normality) is adaptive to both low dimension with tau = 0 and moderate dimension with tau is an element of (0, 1). Our general theory holds, in particular, without Gaussian design/error or structural parameter assumption, and applies to a broad class of quadratic functionals covering all aforementioned applications. As a by-product, we find that the classical fixed-dimensional results continue to hold if and only if the signal-to-noise ratio is large enough, say when pn diverges but slower than n. Extensive numerical results demonstrate the satisfactory performance of the proposed methodology even when pn = 0.9n in some extreme cases. The mathematical arguments are based on the random matrix theory and leave-oneobservation- out method.

关键词： Fraction of variance explained linear regression model Moderate dimension Quadratic functional Signal-to-noise ratio

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Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research

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NEURON 2022年第1期110卷 21-35页

作者： Yu, Zhaoxia Guindani, Michele Grieco, Steven F. Chen, Lujia Holmes, Todd C. Xu, Xiangmin Univ Calif Irvine Donald Bren Sch Informat & Comp Sci Dept Stat Irvine CA 92697 USA Univ Calif Irvine Sch Med Dept Anat & Neurobiol Irvine CA 92697 USA Univ Calif Irvine Sch Med Dept Physiol & Biophys Irvine CA 92697 USA Univ Calif Irvine Dept Biomed Engn Irvine CA 92697 USA Univ Calif Irvine Dept Microbiol & Mol Genet Irvine CA 92697 USA Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA Univ Calif Irvine Ctr Neural Circuit Mapping Irvine CA 92697 USA

In basic neuroscience research, data are often clustered or collected with repeated measures, hence correlated. The most widely used methods such as t test and ANOVA do not take data dependence into account and thus are often misused. This Primer introduces linear and generalized mixed-effects models that consider data dependence and provides clear instruction on how to recognize when they are needed and how to apply them. The appropriate use of mixed-effects models will help researchers improve their experimental design and will lead to data analyses with greater validity and higher reproducibility of the experimental findings.

关键词： clustered data repeated measures linear regression model linear mixed-effects model generalized linear mixed-effects model Bayesian analysis

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Adaptive neuro fuzzy inference system to enhance the classification performance in smart irrigation system

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COMPUTATIONAL INTELLIGENCE 2022年第2期38卷 308-322页

作者： Vivekanandhan, VijayaRangan Sakthivel, Subramaniam Manikandan, Muthaiyan Adhiyamaan Coll Engn Dept CSE Hosur Tamil Nadu India Sona Coll Technol Dept CSE Salem India

Traditional agricultural systems require huge amounts of money for on-site irrigation power. Irrigation is the process of giving water to plants for growth and development. The cost of global warming creates the potential effects of water adaptation measures to ensure food production and water availability for consumption. It requires continuous monitoring of soil moisture content at the root zone and initiating irrigation as per the pre-programmed schedule depending upon the plant's nature, growth, soil type, and environment. Thus, the cost to farmers using conventional drip irrigation has become so high that they have to go manually and monitor the land frequently. So when the previous method was used for irrigation, it did not give correct classification results. Agricultural data mining technology provides the best crops for travel water to increase the number of crops produced. Agricultural data can be collected more efficiently using data collection and capable analytics sensors. This proposed work introduces an adaptive neuro-fuzzy inference system (ANFIS) technique for analyzing agricultural plant growth based on soil, water level, temperature, and moisture conditions. The proposed technique has three phases: environment data collection using sensor node, preprocessing, feature selection, and classification. The initial phase captures the plant's environment condition via a sensor device, then transmits data to servers with the help of an edge node or gateway. This recursive adaptive filter method normalizes the data and removes irrelevant noise data from the sensor node. The feature selection method to extract the environment feature value with the help of a linear regression model. The ANFIS method's plant environment feature value has trained the neurons with the help of the stochastic gradient descent method. The classification method introduces a fuzzy rule to compute and validate the input parameters (e.g., soil moisture, temperature, and humidity) to

关键词： adaptive neuro-fuzzy inference system (ANFIS) environment data collection fuzzy logic controller linear regression model recursive adaptive filter wireless sensor nodes

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Adding a constant to the variables in the regression through the origin: the effect on the uncentered R²

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Quality and Quantity 2023年第3期57卷 2781-2789页

作者： Triacca, Umberto Department of Computer Engineering Computer Science and Mathematics University of L’Aquila Coppito 67010 Italy

It is well known the effect on uncentered R2 stemming from adding a constant to dependent variable in a linear regression model with intercept. In this paper, we investigate the effect of adding a constant to variable... 详细信息

It is well known the effect on uncentered R² stemming from adding a constant to dependent variable in a linear regression model with intercept. In this paper, we investigate the effect of adding a constant to variables on the uncentered R² when a linear regression through the origin is used. In particular, we consider two cases. First, a constant c∈ R is added to all observations of the dependent variable. Second, a constant c∈ R is added to all the observations of both the dependent variable and at least an independent variable. We show that in both cases there is an artificial variation of the uncentered R². This quantity is not invariant under location change. © 2022, The Author(s).

关键词： Coefficient of determination Least squares linear regression model Measure of goodness of fit

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Estimating maize seedling number with UAV RGB images and advanced image processing methods

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PRECISION AGRICULTURE 2022年第5期23卷 1604-1632页

作者： Liu, Shuaibing Yin, Dameng Feng, Haikuan Li, Zhenhai Xu, Xiaobin Shi, Lei Jin, Xiuliang Chinese Acad Agr Sci Inst Crop Sci Beijing 100081 Peoples R China Chinese Acad Agr Sci Natl Nanfan Res Inst Sanya Sanya 572024 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Natl Engn Res Ctr Informat Technol Agr Beijing 100089 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China

Accurately identifying the quantity of maize seedlings is useful in improving maize varieties with high seedling emergence rates in a breeding program. The traditional method is to calculate the number of crops manually, which is labor-intensive and time-consuming. Recently, observation methods utilizing a UAV have been widely employed to monitor crop growth due to their low cost, intuitive nature and ability to collect data without contacting the crop. However, most investigations have lacked a systematic strategy for seedling identification. Additionally, estimating the quantity of maize seedlings is challenging due to the complexity of field crop growth environments. The purpose of this research was to rapidly and automatically count maize seedlings. Three models for estimating the quantity of maize seedlings in the field were developed: corner detection model (C), linear regression model (L) and deep learning model (D). The robustness of these maize seedling counting models was validated using RGB images taken at various dates and locations. The maize seedling recognition rate of the three models were 99.78% (C), 99.9% (L) and 98.45% (D) respectively. The L model can be well adapted to different data to identify the number of maize seedlings. The results indicated that the high-throughput and fast method of calculating the number of maize seedlings is a useful tool for maize phenotyping.

关键词： Maize seedlings Unmanned aerial vehicle Faster R-CNN linear regression model Corner detection model

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Objective Bayesian group variable selection for linear model

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COMPUTATIONAL STATISTICS 2022年第3期37卷 1287-1310页

作者： Kang, Sang Gil Lee, Woo Dong Kim, Yongku Sangji Univ Dept Comp & Data Informat Wonju 26339 South Korea Daegu Haany Univ Premajor Cosmet & Pharmaceut Kyungsan 38610 South Korea Kyungpook Natl Univ Dept Stat Daegu 41566 South Korea

Prediction variables of the regression model are grouped in many application problems. For example, a factor in an analysis of variance can have several levels or each original prediction variable in additive models can be expanded into different order polynomials or a set of basis functions. It is essential to select important groups and individual variables within the selected groups. In this study, we propose the objective Bayesian group and individual variable selections within the selected groups in the regression model to reduce the computational cost, even though the number of regression variables is large. Besides, we examine the consistency of the proposed group variable selection procedure. The proposed objective Bayesian approach is investigated using simulation and real data examples. The comparisons between the penalized regression approaches, Bayesian group lasso and the proposed method are presented.

关键词： Bayes factor Group variable selection Intrinsic prior linear regression model

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Efficient and robust estimation for autoregressive regression models using shape mixtures of skewt normal distribution

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METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY 2022年第3期24卷 1519-1551页

作者： Nduka, Uchenna Chinedu Univ Nigeria Dept Stat Nsukka Enugu State Nigeria

Multiple linear regression model based on normally distributed and uncorrelated errors is a popular statistical tool with application in various fields. But these assumptions of normality and no serial correlation are hardly met in real life. Hence, this study considers the linear regression time series model for series with outliers and autocorrelated errors. These autocorrelated errors are represented by a covariance-stationary autoregressive process where the independent innovations are driven by shape mixture of skew-t normal distribution. The shape mixture of skew-t normal distribution is a flexible extension of the skew-t normal with an additional shape parameter that controls skewness and kurtosis. With this error model, stochastic modeling of multiple outliers is possible with an adaptive robust maximum likelihood estimation of all the parameters. An Expectation Conditional Maximization Either algorithm is developed to carryout the maximum likelihood estimation. We derive asymptotic standard errors of the estimators through an information-based approximation. The performance of the estimation procedure developed is evaluated through Monte Carlo simulations and real life data analysis.

关键词： Autoregressive process Expectation maximization algorithm linear regression model Robust estimation Skew-t-distribution

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Real Estate Forecasting System Using Machine Learning Algorithms 6

Real Estate Forecasting System Using Machine Learning Algori...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Kaur, Harshpreet Kushwanth, A. Sai Praveen, M. Akshay, Y. Kiran, Y. Surya Kartheek, B. Ganesh Kamal Reddy, G. Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350304480

The idea of this paper is to predict the value of houses and properties of the market value. The aim of the presented paper is to estimate the market value of the asset being sold. This application assists in determining a location's beginning pricing based on location-specific factors. The below mentioned idea to find the price of a location based on location variables. The below discussed models helps to find the market value of any property in India through different databases depending upon the locations. The presented paper suggests the buyer in determining the prices of different houses with the similarity of characteristics and properties. According to the survey made the prices of properties always rise every year, so a buyer needs a real estate forecasting system. This research paper can help estimate the price of a house which helps both seller and buyer for a reasonable price to buy a property. The result showed that machine learning algorithms in linear regressions model can accurately predict area price with high accuracy rates. However, some challenges such as property value based on area, characteristics, locality, facilities provided. It highlights the importance of machine learning algorithms for price predictions to research and address the challenges. © 2023 IEEE.

关键词： Data Science Google collab House price prediction linear regression model Machine Learning Python

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Genomic breed composition of Ningxiang pig via different SNP panels

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JOURNAL OF ANIMAL PHYSIOLOGY AND ANIMAL NUTRITION 2022年第4期106卷 783-791页

作者： Gao, Zhendong Zhang, Yuebo Li, Zhi Zeng, Qinhua Yang, Fang Song, Yuexiang Song, Yukun He, Jun Hunan Agr Univ Coll Anim Sci & Technol Changsha 410000 Peoples R China

The genomic breed composition (GBC) reflects the genetic relationship between individual animal and ancestor breeds in composite or hybrid breeds. Also, it can estimate the genomic contribution of each breed (ancestor) to the genome of each individual animal. Using genomic SNP information to estimate Ningxiang pig GBC is of great significance. First of all, GBC was widely used in cattle and had significant effects, but there is almost no using experience in Chinese endemic pig breeds. Importantly, High-density SNPs are expensive but can be economized by deploying a relatively small number of highly informative SNP scattered evenly across the genome. Moreover, the impact of low-density SNPs selection strategy on estimating the GBC of individual animals has not been fully explained. Using SNP data from different databases and organizations, we established reference (N = 2015) and verification (N = 302) data sets. Twelve successively smaller SNP panels (500, 1K, 5K, 10K) were built from those SNP in the reference data by three selection methods (uniform, maximized the Euclidean distance (MED) and random distribution method). For each panel, the GBC of Ningxiang pigs in the reference dataset was estimated. Then combining Shannon entropy and the GBC results, the optimal panel (the 10K SNP panel constructed by MED method) was picked out to estimate the GBC of verification Ningxiang pig, which detected that 230 individuals were purebred Ningxiang pigs and the remaining 72 impure individuals contained 6.44% blood related with Rongchang pigs and 4.09% with Bamaxiang pigs in the verification Ningxiang population. Finally, the genetic structure analysis of verification population was performed combining with the results of GBC, multi-dimensional scaling (MDS) analysis and hierarchical cluster analysis. These results showed: (a) GBC could accurately identify purebred Ningxiang pigs and, scientifically, calculate the genomic contribution of each breed of each hybrid animal. (b)

关键词： genetic structure analysis genomic breed composition linear regression model Ningxiang pig the optimal snp panel

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