scholarly journals Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 473
Author(s):  
Patrik Zajec ◽  
Jože M. Rožanec ◽  
Elena Trajkova ◽  
Inna Novalija ◽  
Klemen Kenda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Active Learning ◽  
Demand Forecasting ◽  
Research Work ◽  
Use Case ◽  
Multiple Strategies ◽  
Machine Learning Model ◽  
The Media ◽  
Gathering Data

This research work describes an architecture for building a system that guides a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in a manufacturing demand forecasting use case and can be extended to other domains. In addition, the system provides the means for knowledge acquisition by gathering data from users. Finally, it implements an active learning component and compares multiple strategies to recommend media news to the user. We compare such strategies through a set of experiments to understand how they balance learning and provide accurate media news recommendations to the user. The media news aims to provide additional context to demand forecasts and enhance judgment on decision-making.

scholarly journals Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing

2021 ◽  
Author(s):  
Patrik Zajec ◽  
Jože M. Rožanec ◽  
Elena Trajkova ◽  
Inna Novalija ◽  
Klemen Kenda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Active Learning ◽  
Knowledge Acquisition ◽  
Demand Forecasting ◽  
Research Work ◽  
Use Case ◽  
Multiple Strategies ◽  
Machine Learning Model ◽  
Gathering Data

This research work describes an architecture for building a system that guide a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in manufacturing demand forecasting use case and can be extended to other domains. In addition, the system provides means for knowledge acquisition by gathering data from users. Finally, it implements an active learning component and compares multiple strategies to recommend media news to the user. Such media news aims to provide additional context to demand forecasts and enhance judgment on decision-making.

scholarly journals Automated Retraining of Machine Learning Models

2019 ◽  
Vol 8 (12) ◽  
pp. 445-452
Keyword(s):  
Machine Learning ◽  
Input Data ◽  
Research Work ◽  
Learning Models ◽  
Machine Learning Methods ◽  
Machine Learning Model ◽  
Crucial Component ◽  
Conventional Machine ◽  
Over Time ◽  
Machine Learning Models

Data is the most crucial component of a successful ML system. Once a machine learning model is developed, it gets obsolete over time due to presence of new input data being generated every second. In order to keep our predictions accurate we need to find a way to keep our models up to date. Our research work involves finding a mechanism which can retrain the model with new data automatically. This research also involves exploring the possibilities of automating machine learning processes. We started this project by training and testing our model using conventional machine learning methods. The outcome was then compared with the outcome of those experiments conducted using the AutoML methods like TPOT. This helped us in finding an efficient technique to retrain our models. These techniques can be used in areas where people do not deal with the actual working of a ML model but only require the outputs of ML processes

scholarly journals Active Learning Approaches for Labeling Text: Review and Assessment of the Performance of Active Learning Approaches

2020 ◽  
Vol 28 (4) ◽  
pp. 532-551
Author(s):  
Blake Miller ◽  
Fridolin Linder ◽  
Walter R. Mebane
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Random Sampling ◽  
Supervised Machine Learning ◽  
Learning Approaches ◽  
Simulation Studies ◽  
Text Data ◽  
Passive Learning ◽  
Machine Learning Model ◽  
The Cost

Supervised machine learning methods are increasingly employed in political science. Such models require costly manual labeling of documents. In this paper, we introduce active learning, a framework in which data to be labeled by human coders are not chosen at random but rather targeted in such a way that the required amount of data to train a machine learning model can be minimized. We study the benefits of active learning using text data examples. We perform simulation studies that illustrate conditions where active learning can reduce the cost of labeling text data. We perform these simulations on three corpora that vary in size, document length, and domain. We find that in cases where the document class of interest is not balanced, researchers can label a fraction of the documents one would need using random sampling (or “passive” learning) to achieve equally performing classifiers. We further investigate how varying levels of intercoder reliability affect the active learning procedures and find that even with low reliability, active learning performs more efficiently than does random sampling.

scholarly journals Retail Giant Sales Forecasting using Machine Learning

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2408-2411
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Making ◽  
Learning Model ◽  
Accurate Prediction ◽  
Machine Learning Model ◽  
Different Types ◽  
Using Data ◽  
Near Future ◽  
Sales Trend

Sales forecasting is widely recognized and plays a major role in an organization’s decision making. It is an integral part in business execution of retail giants, so that they can change their strategy to improve sales in the near future. This helps in better management of their resources like machine, money and manpower. Forecasting the sales will help in managing the revenue and inventory accordingly. This paper proposes a model that can forecast most profitable segments at granular level. As most retail giants have many branches in different locations, consolidation of sales are hard using data mining. Instead using machine learning model helps in getting reliable and accurate results. This paper helps in understanding the sales trend to monitor or predict future applicable on different types of sales patterns and products to produce accurate prediction results.

scholarly journals Machine Learning Based Clinical Diagnosis of Liver Patients with Instance Replacement

2021 ◽  
Author(s):  
J. V. D. Prasad ◽  
A. Raghuvira Pratap ◽  
Babu Sallagundla
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Research Work ◽  
Feature Selection Method ◽  
Learning Model ◽  
Disease Classification ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Huge Data ◽  
Machine Learning Model

With the rapid increase in number of clinical data and hence the prediction and analysing data becomes very difficult. With the help of various machine learning models, it becomes easy to work on these huge data. A machine learning model faces lots of challenges; one among the challenge is feature selection. In this research work, we propose a novel feature selection method based on statistical procedures to increase the performance of the machine learning model. Furthermore, we have tested the feature selection algorithm in liver disease classification dataset and the results obtained shows the efficiency of the proposed method.

scholarly journals Activity recognition through interactive machine learning in a dynamic sensor setting

2020 ◽  
Author(s):  
Agnes Tegen ◽  
Paul Davidsson ◽  
Jan A. Persson
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Strategies ◽  
Activity Recognition ◽  
Feature Space ◽  
Streaming Data ◽  
Interactive Machine Learning ◽  
Machine Learning Model ◽  
Input Variables ◽  
Over Time

Abstract The advances in Internet of things lead to an increased number of devices generating and streaming data. These devices can be useful data sources for activity recognition by using machine learning. However, the set of available sensors may vary over time, e.g. due to mobility of the sensors and technical failures. Since the machine learning model uses the data streams from the sensors as input, it must be able to handle a varying number of input variables, i.e. that the feature space might change over time. Moreover, the labelled data necessary for the training is often costly to acquire. In active learning, the model is given a budget for requesting labels from an oracle, and aims to maximize accuracy by careful selection of what data instances to label. It is generally assumed that the role of the oracle only is to respond to queries and that it will always do so. In many real-world scenarios however, the oracle is a human user and the assumptions are simplifications that might not give a proper depiction of the setting. In this work we investigate different interactive machine learning strategies, out of which active learning is one, which explore the effects of an oracle that can be more proactive and factors that might influence a user to provide or withhold labels. We implement five interactive machine learning strategies as well as hybrid versions of them and evaluate them on two datasets. The results show that a more proactive user can improve the performance, especially when the user is influenced by the accuracy of earlier predictions. The experiments also highlight challenges related to evaluating performance when the set of classes is changing over time.

scholarly journals Machine Learning Approach to Decision Making for Insulin Initiation in Japanese Patients With Type 2 Diabetes (JDDM 58): Model Development and Validation Study (Preprint)

2020 ◽  
Author(s):  
Kazuya Fujihara ◽  
Yasuhiro Matsubayashi ◽  
Mayuko Harada Yamada ◽  
Masahiko Yamamoto ◽  
Toshihiro Iizuka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Logistic Regression ◽  
Gold Standard ◽  
Learning Approach ◽  
Insulin Initiation ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
General Physicians

BACKGROUND Applications of machine learning for the early detection of diseases for which a clear-cut diagnostic gold standard exists have been evaluated. However, little is known about the usefulness of machine learning approaches in the decision-making process for decisions such as insulin initiation by diabetes specialists for which no absolute standards exist in clinical settings. OBJECTIVE The objectives of this study were to examine the ability of machine learning models to predict insulin initiation by specialists and whether the machine learning approach could support decision making by general physicians for insulin initiation in patients with type 2 diabetes. METHODS Data from patients prescribed hypoglycemic agents from December 2009 to March 2015 were extracted from diabetes specialists’ registries, resulting in a sample size of 4860 patients who had received initial monotherapy with either insulin (n=293) or noninsulin (n=4567). Neural network output was insulin initiation ranging from 0 to 1 with a cutoff of >0.5 for the dichotomous classification. Accuracy, recall, and area under the receiver operating characteristic curve (AUC) were calculated to compare the ability of machine learning models to make decisions regarding insulin initiation to the decision-making ability of logistic regression and general physicians. By comparing the decision-making ability of machine learning and logistic regression to that of general physicians, 7 cases were chosen based on patient information as the gold standard based on the agreement of 8 of the 9 specialists. RESULTS The AUCs, accuracy, and recall of logistic regression were higher than those of machine learning (AUCs of 0.89-0.90 for logistic regression versus 0.67-0.74 for machine learning). When the examination was limited to cases receiving insulin, discrimination by machine learning was similar to that of logistic regression analysis (recall of 0.05-0.68 for logistic regression versus 0.11-0.52 for machine learning). Accuracies of logistic regression, a machine learning model (downsampling ratio of 1:8), and general physicians were 0.80, 0.70, and 0.66, respectively, for 43 randomly selected cases. For the 7 gold standard cases, the accuracies of logistic regression and the machine learning model were 1.00 and 0.86, respectively, with a downsampling ratio of 1:8, which were higher than the accuracy of general physicians (ie, 0.43). CONCLUSIONS Although we found no superior performance of machine learning over logistic regression, machine learning had higher accuracy in prediction of insulin initiation than general physicians, defined by diabetes specialists’ choice of the gold standard. Further study is needed before the use of machine learning–based decision support systems for insulin initiation can be incorporated into clinical practice.

scholarly journals A New Ensenble Learning based Optimal Prediction Model for Cardiovascular Diseases

2021 ◽  
Vol 309 ◽  
pp. 01007
Author(s):  
B. Srınıvasa Rao
Keyword(s):  
Machine Learning ◽  
Cardiovascular Diseases ◽  
Present Model ◽  
Research Work ◽  
Learning Model ◽  
Ensemble Technique ◽  
Machine Learning Model ◽  
Learning Technique ◽  
Traditional Approaches ◽  
Optimal Machine

The present paperreports an optimal machine learning model for an effective prediction of cardiovascular diseases that uses the ensemble learning technique. The present research work gives an insight about the coherent way of combining Naive Bayes and Random Forest algorithm using ensemble technique. It also discusses how the present model is different from other traditional approaches. The present experimental results manifest that the present optimal machine learning model is more efficient than the other models.

A Machine Learning Model for Advanced Decision Making in Smart Education Systems

2021 ◽  
pp. 191-220
Author(s):  
Palanivel Kuppusamy ◽  
Suresh Joseph K.
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Education System ◽  
Learning Model ◽  
Heterogeneous Data ◽  
Educational Systems ◽  
Educational Administrators ◽  
Educational Processes ◽  
Machine Learning Model ◽  
Smart Education

A smart education system uses emerging technologies and generates a vast amount of heterogeneous data in the learning environment. The conventional methods presently used by the educational administrators for decision-making are minimal and take more time to generate the results. The educational administrators could not be able to predict the results quickly and advance for better decision-making. Today, artificial intelligence approaches are widely used in educational systems for automating educational processes. These approaches achieve a better, efficient, and effective modern education system. Integrating machine learning deep learning techniques with a smart education system can automatically analyze the generated data for better decision-making and provide recommendations to students and educational administrators. This chapter aims to introduce a machine learning model to predict the outcomes in a smart education system.

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