scholarly journals Sequential learning to accelerate discovery of alkali-activated binders

2021 ◽  
Author(s):  
Christoph Völker ◽  
Rafia Firdous ◽  
Dietmar Stephan ◽  
Sabine Kruschwitz
Keyword(s):  
Machine Learning ◽  
Training Data ◽  
Sequential Learning ◽  
Learning Models ◽  
Data Set ◽  
Optimal Mix ◽  
Order Of Magnitude ◽  
Alkali Activated Binders ◽  
Alkali Activated ◽  
Machine Learning Models

AbstractAlkali-activated binders (AAB) can provide a clean alternative to conventional cement in terms of CO2 emissions. However, as yet there are no sufficiently accurate material models to effectively predict the AAB properties, thus making optimal mix design highly costly and reducing the attractiveness of such binders. This work adopts sequential learning (SL) in high-dimensional material spaces (consisting of composition and processing data) to find AABs that exhibit desired properties. The SL approach combines machine learning models and feedback from real experiments. For this purpose, 131 data points were collected from different publications. The data sources are described in detail, and the differences between the binders are discussed. The sought-after target property is the compressive strength of the binders after 28 days. The success is benchmarked in terms of the number of experiments required to find materials with the desired strength. The influence of some constraints was systematically analyzed, e.g., the possibility to parallelize the experiments, the influence of the chosen algorithm and the size of the training data set. The results show the advantage of SL, i.e., the amount of data required can potentially be reduced by at least one order of magnitude compared to traditional machine learning models, while at the same time exploiting highly complex information. This brings applications in laboratory practice within reach.

scholarly journals Thinking Globally, Acting Locally: On the Issue of Training Set Imbalance and the Case for Local Machine Learning Models in Chemistry

2019 ◽  
Author(s):  
Mojtaba Haghighatlari ◽  
Ching-Yen Shih ◽  
Johannes Hachmann
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Imbalanced Data ◽  
Clean Energy ◽  
Training Data ◽  
Learning Models ◽  
Data Set ◽  
Global Models ◽  
Machine Learning Models ◽  
Training Sets

<div><div><div><p>The appropriate sampling of training data out of a potentially imbalanced data set is of critical importance for the development of robust and accurate machine learning models. A challenge that underpins this task is the partitioning of the data into groups of similar instances, and the analysis of the group populations. In molecular data sets, different groups of molecules may be hard to identify. However, if the distribution of a given data set is ignored then some of these groups may remain under-represented and the sampling biased, even if the size of data is large. In this study, we use the example of the Harvard Clean Energy Project (CEP) data set to assess the challenges posed by imbalanced data and the impact that accounting for different groups during the selection of training data has on the quality of the resulting machine learning models. We employ a partitioning criterion based on the underlying rules for the CEP molecular library generation to identify groups of structurally similar compounds. First, we evaluate the performance of regression models that are trained globally (i.e., by randomly sampling the entire data set for training data). This traditional approach serves as the benchmark reference. We compare its results with those of models that are trained locally, i.e., within each of the identified molecular domains. We demonstrate that local models outperform the best reported global models by considerable margins and are more efficient in their training data needs. We propose a strategy to redesign training sets for the development of improved global models. While the resulting uniform training sets can successfully yield robust global models, we identify the distribution mismatch between feature representations of different molecular domains as a critical limitation for any further improvement. We take advantage of the discovered distribution shift and propose an ensemble of classification and regression models to achieve a generalized and reliable model that outperforms the state-of-the-art model, trained on the CEP data set. Moreover, this study provides a benchmark for the development of future methodologies concerned with imbalanced chemical data.</p></div></div></div>

scholarly journals Thinking Globally, Acting Locally: On the Issue of Training Set Imbalance and the Case for Local Machine Learning Models in Chemistry

2019 ◽  
Author(s):  
Mojtaba Haghighatlari ◽  
Ching-Yen Shih ◽  
Johannes Hachmann
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Imbalanced Data ◽  
Clean Energy ◽  
Training Data ◽  
Learning Models ◽  
Data Set ◽  
Global Models ◽  
Machine Learning Models ◽  
Training Sets

<div><div><div><p>The appropriate sampling of training data out of a potentially imbalanced data set is of critical importance for the development of robust and accurate machine learning models. A challenge that underpins this task is the partitioning of the data into groups of similar instances, and the analysis of the group populations. In molecular data sets, different groups of molecules may be hard to identify. However, if the distribution of a given data set is ignored then some of these groups may remain under-represented and the sampling biased, even if the size of data is large. In this study, we use the example of the Harvard Clean Energy Project (CEP) data set to assess the challenges posed by imbalanced data and the impact that accounting for different groups during the selection of training data has on the quality of the resulting machine learning models. We employ a partitioning criterion based on the underlying rules for the CEP molecular library generation to identify groups of structurally similar compounds. First, we evaluate the performance of regression models that are trained globally (i.e., by randomly sampling the entire data set for training data). This traditional approach serves as the benchmark reference. We compare its results with those of models that are trained locally, i.e., within each of the identified molecular domains. We demonstrate that local models outperform the best reported global models by considerable margins and are more efficient in their training data needs. We propose a strategy to redesign training sets for the development of improved global models. While the resulting uniform training sets can successfully yield robust global models, we identify the distribution mismatch between feature representations of different molecular domains as a critical limitation for any further improvement. We take advantage of the discovered distribution shift and propose an ensemble of classification and regression models to achieve a generalized and reliable model that outperforms the state-of-the-art model, trained on the CEP data set. Moreover, this study provides a benchmark for the development of future methodologies concerned with imbalanced chemical data.</p></div></div></div>

scholarly journals Thinking Globally, Acting Locally: On the Issue of Training Set Imbalance and the Case for Local Machine Learning Models in Chemistry

2019 ◽  
Author(s):  
Mojtaba Haghighatlari ◽  
Ching-Yen Shih ◽  
Johannes Hachmann
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Imbalanced Data ◽  
Clean Energy ◽  
Training Data ◽  
Learning Models ◽  
Data Set ◽  
Global Models ◽  
Machine Learning Models ◽  
Training Sets

<div><div><div><p>The appropriate sampling of training data out of a potentially imbalanced data set is of critical importance for the development of robust and accurate machine learning models. A challenge that underpins this task is the partitioning of the data into groups of similar instances, and the analysis of the group populations. In molecular data sets, different groups of molecules may be hard to identify. However, if the distribution of a given data set is ignored then some of these groups may remain under-represented and the sampling biased, even if the size of data is large. In this study, we use the example of the Harvard Clean Energy Project (CEP) data set to assess the challenges posed by imbalanced data and the impact that accounting for different groups during the selection of training data has on the quality of the resulting machine learning models. We employ a partitioning criterion based on the underlying rules for the CEP molecular library generation to identify groups of structurally similar compounds. First, we evaluate the performance of regression models that are trained globally (i.e., by randomly sampling the entire data set for training data). This traditional approach serves as the benchmark reference. We compare its results with those of models that are trained locally, i.e., within each of the identified molecular domains. We demonstrate that local models outperform even the best global models by considerable margins and are more efficient in their training data needs. We propose a strategy to redesign training sets for the development of improved global models. While the resulting uniform training sets can successfully yield robust global models, we identify the distribution mismatch between feature representations of different molecular domains as a critical limitation for any further improvement. We take advantage of the discovered distribution shift and propose an ensemble of classification and regression models to achieve generalized and reliable models across the CEP data set. This study provides a benchmark for the development of future methodologies concerned with imbalanced chemical data.</p></div></div></div>

scholarly journals Machine Learning in Futures Markets

2021 ◽  
Vol 14 (3) ◽  
pp. 119
Author(s):  
Fabian Waldow ◽  
Matthias Schnaubelt ◽  
Christopher Krauss ◽  
Thomas Günter Fischer
Keyword(s):  
Machine Learning ◽  
Futures Markets ◽  
Learning Models ◽  
Cross Sectional ◽  
Data Set ◽  
Statistical Arbitrage ◽  
Out Of Sample ◽  
Sample Testing ◽  
Arbitrage Strategy ◽  
Machine Learning Models

In this paper, we demonstrate how a well-established machine learning-based statistical arbitrage strategy can be successfully transferred from equity to futures markets. First, we preprocess futures time series comprised of front months to render them suitable for our returns-based trading framework and compile a data set comprised of 60 futures covering nearly 10 trading years. Next, we train several machine learning models to predict whether the h-day-ahead return of each future out- or underperforms the corresponding cross-sectional median return. Finally, we enter long/short positions for the top/flop-k futures for a duration of h days and assess the financial performance of the resulting portfolio in an out-of-sample testing period. Thereby, we find the machine learning models to yield statistically significant out-of-sample break-even transaction costs of 6.3 bp—a clear challenge to the semi-strong form of market efficiency. Finally, we discuss sources of profitability and the robustness of our findings.

scholarly journals High performance logistic regression for privacy-preserving genome analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Martine De Cock ◽  
Rafael Dowsley ◽  
Anderson C. A. Nascimento ◽  
Davis Railsback ◽  
Jianwei Shen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Genome Analysis ◽  
Local Area Network ◽  
Local Area ◽  
Activation Function ◽  
Area Network ◽  
Learning Models ◽  
Data Set ◽  
Machine Learning Models

Abstract Background In biomedical applications, valuable data is often split between owners who cannot openly share the data because of privacy regulations and concerns. Training machine learning models on the joint data without violating privacy is a major technology challenge that can be addressed by combining techniques from machine learning and cryptography. When collaboratively training machine learning models with the cryptographic technique named secure multi-party computation, the price paid for keeping the data of the owners private is an increase in computational cost and runtime. A careful choice of machine learning techniques, algorithmic and implementation optimizations are a necessity to enable practical secure machine learning over distributed data sets. Such optimizations can be tailored to the kind of data and Machine Learning problem at hand. Methods Our setup involves secure two-party computation protocols, along with a trusted initializer that distributes correlated randomness to the two computing parties. We use a gradient descent based algorithm for training a logistic regression like model with a clipped ReLu activation function, and we break down the algorithm into corresponding cryptographic protocols. Our main contributions are a new protocol for computing the activation function that requires neither secure comparison protocols nor Yao’s garbled circuits, and a series of cryptographic engineering optimizations to improve the performance. Results For our largest gene expression data set, we train a model that requires over 7 billion secure multiplications; the training completes in about 26.90 s in a local area network. The implementation in this work is a further optimized version of the implementation with which we won first place in Track 4 of the iDASH 2019 secure genome analysis competition. Conclusions In this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function. To the best of our knowledge, we present the fastest existing secure multi-party computation implementation for training logistic regression models on high dimensional genome data distributed across a local area network.

scholarly journals A Physics-Infused Deep Learning Model for the Prediction of Refractive Indices and Its Use for the Large-Scale Screening of Organic Compound Space

2019 ◽  
Author(s):  
Mojtaba Haghighatlari ◽  
Gaurav Vishwakarma ◽  
Mohammad Atif Faiz Afzal ◽  
Johannes Hachmann
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Large Scale ◽  
Organic Molecules ◽  
Learning Model ◽  
Training Data ◽  
Refractive Indices ◽  
Learning Models ◽  
Deep Learning Model ◽  
Machine Learning Models

<div><div><div><p>We present a multitask, physics-infused deep learning model to accurately and efficiently predict refractive indices (RIs) of organic molecules, and we apply it to a library of 1.5 million compounds. We show that it outperforms earlier machine learning models by a significant margin, and that incorporating known physics into data-derived models provides valuable guardrails. Using a transfer learning approach, we augment the model to reproduce results consistent with higher-level computational chemistry training data, but with a considerably reduced number of corresponding calculations. Prediction errors of machine learning models are typically smallest for commonly observed target property values, consistent with the distribution of the training data. However, since our goal is to identify candidates with unusually large RI values, we propose a strategy to boost the performance of our model in the remoter areas of the RI distribution: We bias the model with respect to the under-represented classes of molecules that have values in the high-RI regime. By adopting a metric popular in web search engines, we evaluate our effectiveness in ranking top candidates. We confirm that the models developed in this study can reliably predict the RIs of the top 1,000 compounds, and are thus able to capture their ranking. We believe that this is the first study to develop a data-derived model that ensures the reliability of RI predictions by model augmentation in the extrapolation region on such a large scale. These results underscore the tremendous potential of machine learning in facilitating molecular (hyper)screening approaches on a massive scale and in accelerating the discovery of new compounds and materials, such as organic molecules with high-RI for applications in opto-electronics.</p></div></div></div>

scholarly journals Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7834
Author(s):  
Christopher Hecht ◽  
Jan Figgener ◽  
Dirk Uwe Sauer
Keyword(s):  
Machine Learning ◽  
Binary Data ◽  
Training Data ◽  
Gradient Boosting ◽  
Traffic Density ◽  
Learning Models ◽  
Charging Infrastructure ◽  
Ensemble Models ◽  
Charging Station ◽  
Machine Learning Models

Electric vehicles may reduce greenhouse gas emissions from individual mobility. Due to the long charging times, accurate planning is necessary, for which the availability of charging infrastructure must be known. In this paper, we show how the occupation status of charging infrastructure can be predicted for the next day using machine learning models— Gradient Boosting Classifier and Random Forest Classifier. Since both are ensemble models, binary training data (occupied vs. available) can be used to provide a certainty measure for predictions. The prediction may be used to adapt prices in a high-load scenario, predict grid stress, or forecast available power for smart or bidirectional charging. The models were chosen based on an evaluation of 13 different, typically used machine learning models. We show that it is necessary to know past charging station usage in order to predict future usage. Other features such as traffic density or weather have a limited effect. We show that a Gradient Boosting Classifier achieves 94.8% accuracy and a Matthews correlation coefficient of 0.838, making ensemble models a suitable tool. We further demonstrate how a model trained on binary data can perform non-binary predictions to give predictions in the categories “low likelihood” to “high likelihood”.

scholarly journals Comparison of Resampling Algorithms to Address Class Imbalance when Developing Machine Learning Models to Predict Foodborne Pathogen Presence in Agricultural Water

2021 ◽  
Vol 9 ◽  
Author(s):  
Daniel Lowell Weller ◽  
Tanzy M. T. Love ◽  
Martin Wiedmann
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Predictive Models ◽  
Training Data ◽  
Agricultural Water ◽  
Learning Models ◽  
Safety Hazards ◽  
E Coli ◽  
Resampling Method ◽  
Machine Learning Models

Recent studies have shown that predictive models can supplement or provide alternatives to E. coli-testing for assessing the potential presence of food safety hazards in water used for produce production. However, these studies used balanced training data and focused on enteric pathogens. As such, research is needed to determine 1) if predictive models can be used to assess Listeria contamination of agricultural water, and 2) how resampling (to deal with imbalanced data) affects performance of these models. To address these knowledge gaps, this study developed models that predict nonpathogenic Listeria spp. (excluding L. monocytogenes) and L. monocytogenes presence in agricultural water using various combinations of learner (e.g., random forest, regression), feature type, and resampling method (none, oversampling, SMOTE). Four feature types were used in model training: microbial, physicochemical, spatial, and weather. “Full models” were trained using all four feature types, while “nested models” used between one and three types. In total, 45 full (15 learners*3 resampling approaches) and 108 nested (5 learners*9 feature sets*3 resampling approaches) models were trained per outcome. Model performance was compared against baseline models where E. coli concentration was the sole predictor. Overall, the machine learning models outperformed the baseline E. coli models, with random forests outperforming models built using other learners (e.g., rule-based learners). Resampling produced more accurate models than not resampling, with SMOTE models outperforming, on average, oversampling models. Regardless of resampling method, spatial and physicochemical water quality features drove accurate predictions for the nonpathogenic Listeria spp. and L. monocytogenes models, respectively. Overall, these findings 1) illustrate the need for alternatives to existing E. coli-based monitoring programs for assessing agricultural water for the presence of potential food safety hazards, and 2) suggest that predictive models may be one such alternative. Moreover, these findings provide a conceptual framework for how such models can be developed in the future with the ultimate aim of developing models that can be integrated into on-farm risk management programs. For example, future studies should consider using random forest learners, SMOTE resampling, and spatial features to develop models to predict the presence of foodborne pathogens, such as L. monocytogenes, in agricultural water when the training data is imbalanced.

scholarly journals Benchmarking of Machine Learning Models to Assist the Prognosis of Tuberculosis

2021 ◽  
Author(s):  
Maicon Herverton Lino Ferreira da Silva Barros ◽  
Geovanne Oliveira Alves ◽  
Lubnnia Morais Florêncio Souza ◽  
Élisson da Silva Rocha ◽  
João Fausto Lorenzato de Oliveira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Symptoms ◽  
Treatment Decision ◽  
Gradient Boosting ◽  
Original Form ◽  
Learning Models ◽  
Data Set ◽  
Risk Of Death ◽  
Increased Risk ◽  
Machine Learning Models

Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1,139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-layer Perceptron (MLP) models is the best model to predict the cure class.

Building-damage detection method based on machine learning utilizing aerial photographs of the Kumamoto earthquake

2020 ◽  
Vol 36 (3) ◽  
pp. 1166-1187 ◽  
Author(s):  
Shohei Naito ◽  
Hiromitsu Tomozawa ◽  
Yuji Mori ◽  
Takeshi Nagata ◽  
Naokazu Monma ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Training Data ◽  
Aerial Photographs ◽  
Learning Models ◽  
Visual Interpretation ◽  
Damage Classification ◽  
Kumamoto Earthquake ◽  
Machine Learning Models

This article presents a method for detecting damaged buildings in the event of an earthquake using machine learning models and aerial photographs. We initially created training data for machine learning models using aerial photographs captured around the town of Mashiki immediately after the main shock of the 2016 Kumamoto earthquake. All buildings are classified into one of the four damage levels by visual interpretation. Subsequently, two damage discrimination models are developed: a bag-of-visual-words model and a model based on a convolutional neural network. Results are compared and validated in terms of accuracy, revealing that the latter model is preferable. Moreover, for the convolutional neural network model, the target areas are expanded and the recalls of damage classification at the four levels range approximately from 66% to 81%.

Sign in / Sign up

Export Citation Format

Share Document