scholarly journals Research Trend of Causal Machine Learning Method: A Literature Review

2020 ◽  
Vol 9 (2) ◽  
pp. 111-118
Author(s):  
Shindy Arti ◽  
Indriana Hidayah ◽  
Sri Suning Kusumawardhani
Keyword(s):  
Machine Learning ◽  
Learning System ◽  
Research Trend ◽  
Learning Approaches ◽  
Algorithm Performance ◽  
Combination Technique ◽  
Causal Impact ◽  
Learning Research ◽  
The Relationship ◽  
Selection Of

Machine learning is commonly used to predict and implement  pattern recognition and the relationship between variables. Causal machine learning combines approaches for analyzing the causal impact of intervention on the result, asumming a considerably ambigous variables. The combination technique of causality and machine learning is adequate for predicting and understanding the cause and effect of the results. The aim of this study is a systematic review to identify which causal machine learning approaches are generally used. This paper focuses on what data characteristics are applied to causal machine learning research and how to assess the output of algorithms used in the context of causal machine learning research. The review paper analyzes 20 papers with various approaches. This study categorizes data characteristics based on the type of data, attribute value, and the data dimension. The Bayesian Network (BN) commonly used in the context of causality. Meanwhile, the propensity score is the most extensively used in causality research. The variable value will affect algorithm performance. This review can be as a guide in the selection of a causal machine learning system.

Machine learning approaches to understand and predict rate constants for organic processes in mixtures containing ionic liquids

2021 ◽  
Vol 23 (4) ◽  
pp. 2742-2752
Author(s):  
Tamar L. Greaves ◽  
Karin S. Schaffarczyk McHale ◽  
Raphael F. Burkart-Radke ◽  
Jason B. Harper ◽  
Tu C. Le
Keyword(s):  
Machine Learning ◽  
Ionic Liquids ◽  
Rate Constants ◽  
Learning Approaches ◽  
Learning Models ◽  
Organic Reaction ◽  
Machine Learning Models ◽  
Selection Of

Machine learning models were developed for an organic reaction in ionic liquids and validated on a selection of ionic liquids.

Interactive Learning with Mutual Explanations in Relational Domains

2021 ◽  
pp. 338-354
Author(s):  
Ute Schmid
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Inductive Logic Programming ◽  
Interactive Learning ◽  
Inductive Logic ◽  
Learning System ◽  
Real World Data ◽  
Relational Domains ◽  
Learning Research ◽  
Applications Of Machine Learning

With the growing number of applications of machine learning in complex real-world domains machine learning research has to meet new requirements to deal with the imperfections of real world data and the legal as well as ethical obligations to make classifier decisions transparent and comprehensible. In this contribution, arguments for interpretable and interactive approaches to machine learning are presented. It is argued that visual explanations are often not expressive enough to grasp critical information which relies on relations between different aspects or sub-concepts. Consequently, inductive logic programming (ILP) and the generation of verbal explanations from Prolog rules is advocated. Interactive learning in the context of ILP is illustrated with the Dare2Del system which helps users to manage their digital clutter. It is shown that verbal explanations overcome the explanatory one-way street from AI system to user. Interactive learning with mutual explanations allows the learning system to take into account not only class corrections but also corrections of explanations to guide learning. We propose mutual explanations as a building-block for human-like computing and an important ingredient for human AI partnership.

scholarly journals Analysis of Naıve Bayes Algorithm for Email Spam Filtering

2021 ◽  
Vol 7 (01) ◽  
pp. 5-9
Author(s):  
RajKishore Sahni
Keyword(s):  
Machine Learning ◽  
Service Providers ◽  
Machine Learning Techniques ◽  
Research Trend ◽  
Learning Approaches ◽  
Spam Filtering ◽  
Internet Service ◽  
Learning Techniques ◽  
Bayes Algorithm ◽  
Email Spam

The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters. Machine learning methods of recent are being used to successfully detect and filter spam emails. We present a systematic review of some of the popular machine learning based email spam filtering approaches. Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering. The preliminary discussion in the study background examines the applications of machine learning techniques to the email spam filtering process of the leading internet service providers (ISPs) like Gmail, Yahoo and Outlook emails spam filters. Discussion on general email spam filtering process, and the various efforts by different researchers in combating spam through the use machine learning techniques was done. Our review compares the strengths and drawbacks of existing machine learning approaches and the open research problems in spam filtering. We recommended deep learning and deep adversarial learning as the future techniques that can effectively handle the menace of spam emails

scholarly journals Analytical framework for optimized feature extraction for upgrading occupancy sensing performance

2020 ◽  
Vol 10 (4) ◽  
pp. 4093
Author(s):  
Preethi Krishna Rao Mane ◽  
K. Narasimha Rao
Keyword(s):  
Machine Learning ◽  
Energy Management ◽  
Doppler Radar ◽  
Analytical Framework ◽  
Learning Approaches ◽  
Analytical Methodology ◽  
Efficient Selection ◽  
Accuracy Performance ◽  
Selection Of ◽  
Sensing Performance

The adoption of the occupancy sensors has become an inevitable in commercial and non-commercial security devices, owing to their proficiency in the energy management. It has been found that the usages of conventional sensors is shrouded with operational problems, hence the use of the Doppler radar offers better mitigation of such problems. However, the usage of Doppler radar towards occupancy sensing in existing system is found to be very much in infancy stage. Moreover, the performance of monitoring using Doppler radar is yet to be improved more. Therefore, this paper introduces a simplified framework for enriching the event sensing performance by efficient selection of minimal robust attributes using Doppler radar. Adoption of analytical methodology has been carried out to find that different machine learning approaches could be further used for improving the accuracy performance for the feature that has been extracted in the proposed system of occuancy system.

scholarly journals Development of a Recognition System for Spraying Areas from Unmanned Aerial Vehicles Using a Machine Learning Approach

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 313 ◽  
Author(s):  
Pengbo Gao ◽  
Yan Zhang ◽  
Linhuan Zhang ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Machine Learning ◽  
Field Experiments ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Learning System ◽  
Computational Time ◽  
Learning Approaches ◽  
Online Systems ◽  
Online Recognition ◽  
Spray System

Unmanned aerial vehicle (UAV)-based spraying systems have recently become important for the precision application of pesticides, using machine learning approaches. Therefore, the objective of this research was to develop a machine learning system that has the advantages of high computational speed and good accuracy for recognizing spray and non-spray areas for UAV-based sprayers. A machine learning system was developed by using the mutual subspace method (MSM) for images collected from a UAV. Two target lands: agricultural croplands and orchard areas, were considered in building two classifiers for distinguishing spray and non-spray areas. The field experiments were conducted in target areas to train and test the system by using a commercial UAV (DJI Phantom 3 Pro) with an onboard 4K camera. The images were collected from low (5 m) and high (15 m) altitudes for croplands and orchards, respectively. The recognition system was divided into offline and online systems. In the offline recognition system, 74.4% accuracy was obtained for the classifiers in recognizing spray and non-spray areas for croplands. In the case of orchards, the average classifier recognition accuracy of spray and non-spray areas was 77%. On the other hand, the online recognition system performance had an average accuracy of 65.1% for croplands, and 75.1% for orchards. The computational time for the online recognition system was minimal, with an average of 0.0031 s for classifier recognition. The developed machine learning system had an average recognition accuracy of 70%, which can be implemented in an autonomous UAV spray system for recognizing spray and non-spray areas for real-time applications.

scholarly journals Optimized Classification Predictions with a New Index Combining Machine Learning Algorithms

2018 ◽  
Vol 27 (03) ◽  
pp. 1850012 ◽  
Author(s):  
Androniki Tamvakis ◽  
Christos-Nikolaos Anagnostopoulos ◽  
George Tsirtsis ◽  
Antonios D. Niros ◽  
Sofie Spatharis
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Individual Performance ◽  
Optimal Combination ◽  
Machine Learning Algorithms ◽  
Combined Effect ◽  
Different Types ◽  
Open Issue ◽  
The Relationship ◽  
Selection Of

Voting is a commonly used ensemble method aiming to optimize classification predictions by combining results from individual base classifiers. However, the selection of appropriate classifiers to participate in voting algorithm is currently an open issue. In this study we developed a novel Dissimilarity-Performance (DP) index which incorporates two important criteria for the selection of base classifiers to participate in voting: their differential response in classification (dissimilarity) when combined in triads and their individual performance. To develop this empirical index we firstly used a range of different datasets to evaluate the relationship between voting results and measures of dissimilarity among classifiers of different types (rules, trees, lazy classifiers, functions and Bayes). Secondly, we computed the combined effect on voting performance of classifiers with different individual performance and/or diverse results in the voting performance. Our DP index was able to rank the classifier combinations according to their voting performance and thus to suggest the optimal combination. The proposed index is recommended for individual machine learning users as a preliminary tool to identify which classifiers to combine in order to achieve more accurate classification predictions avoiding computer intensive and time-consuming search.

scholarly journals Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling

BJR|Open ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 20190021 ◽  
Author(s):  
Yi Luo ◽  
Huan-Hsin Tseng ◽  
Sunan Cui ◽  
Lise Wei ◽  
Randall K. Ten Haken ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Decision Making ◽  
Radiation Treatment ◽  
Clinical Decision ◽  
Black Box ◽  
Prediction Performance ◽  
Learning Approaches ◽  
Treatment Plans ◽  
The Relationship ◽  
Review Current

Radiation outcomes prediction (ROP) plays an important role in personalized prescription and adaptive radiotherapy. A clinical decision may not only depend on an accurate radiation outcomes’ prediction, but also needs to be made based on an informed understanding of the relationship among patients’ characteristics, radiation response and treatment plans. As more patients’ biophysical information become available, machine learning (ML) techniques will have a great potential for improving ROP. Creating explainable ML methods is an ultimate task for clinical practice but remains a challenging one. Towards complete explainability, the interpretability of ML approaches needs to be first explored. Hence, this review focuses on the application of ML techniques for clinical adoption in radiation oncology by balancing accuracy with interpretability of the predictive model of interest. An ML algorithm can be generally classified into an interpretable (IP) or non-interpretable (NIP) (“black box”) technique. While the former may provide a clearer explanation to aid clinical decision-making, its prediction performance is generally outperformed by the latter. Therefore, great efforts and resources have been dedicated towards balancing the accuracy and the interpretability of ML approaches in ROP, but more still needs to be done. In this review, current progress to increase the accuracy for IP ML approaches is introduced, and major trends to improve the interpretability and alleviate the “black box” stigma of ML in radiation outcomes modeling are summarized. Efforts to integrate IP and NIP ML approaches to produce predictive models with higher accuracy and interpretability for ROP are also discussed.

scholarly journals Human Computation vs. Machine Learning: an Experimental Comparison for Image Classification

2018 ◽  
Vol 5 ◽  
pp. 13-30
Author(s):  
Gloria Re Calegari ◽  
Gioele Nasi ◽  
Irene Celino
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Learning System ◽  
Human Computation ◽  
Experimental Comparison ◽  
Learning Approaches ◽  
Domain Experts ◽  
To Come ◽  
Combined Strategy

Image classification is a classical task heavily studied in computer vision and widely required in many concrete scientific and industrial scenarios. Is it better to rely on human eyes, thus asking people to classify pictures, or to train a machine learning system to automatically solve the task? The answer largely depends on the specific case and the required accuracy: humans may be more reliable - especially if they are domain experts - but automatic processing can be cheaper, even if less capable to demonstrate an "intelligent" behaviour.In this paper, we present an experimental comparison of different Human Computation and Machine Learning approaches to solve the same image classification task on a set of pictures used in light pollution research. We illustrate the adopted methods and the obtained results and we compare and contrast them in order to come up with a long term combined strategy to address the specific issue at scale: while it is hard to ensure a long-term engagement of users to exclusively rely on the Human Computation approach, the human classification is indispensable to overcome the "cold start" problem of automated data modelling.

scholarly journals Modelling of S&P 500 Index Price Based on U.S. Economic Indicators: Machine Learning Approach

2021 ◽  
Vol 32 (4) ◽  
pp. 362-375
Author(s):  
Ligita Gasparėnienė ◽  
Rita Remeikiene ◽  
Aleksejus Sosidko ◽  
Vigita Vėbraitė
Keyword(s):  
Machine Learning ◽  
Statistical Methods ◽  
Stock Prices ◽  
Statistical Significance ◽  
Economic Indicators ◽  
Learning Approaches ◽  
Statistical Machine Learning ◽  
New Methods ◽  
Machine Learning Approach ◽  
Selection Of

In order to forecast stock prices based on economic indicators, many studies have been conducted using well-known statistical methods. Meanwhile, since ~2010 as the power of computers improved, new methods of machine learning began to be used. It would be interesting to know how those algorithms using a variety of mathematical and statistical methods, are able to predict the stock market. The purpose of this article is to model the monthly price of the S&P 500 index based on U.S. economic indicators using statistical, machine learning, deep learning approaches and finally compare metrics of those models. After the selection of indicators according to the data visualization, multicollinearity tests, statistical significance tests, 3 out of 27 indicators remained. The main finding of the research is that the authors improved the baseline statistical linear regression model by 19 percent using a ML Random Forest algorithm. In this way, model achieved accuracy 97.68% of prediction S&P 500 index.

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