On the state of the art in machine learning: A personal review

As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.

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A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach

Applied Sciences ◽

10.3390/app11178074 ◽

2021 ◽

Vol 11 (17) ◽

pp. 8074

Author(s):

Tierui Zou ◽

Nader Aljohani ◽

Keerthiraj Nagaraj ◽

Sheng Zou ◽

Cody Ruben ◽

...

Keyword(s):

Machine Learning ◽

Power Systems ◽

State Of The Art ◽

Real Life ◽

The State ◽

Wide Area ◽

Network Parameter ◽

False Data Injection ◽

Network Parameters ◽

Injection Attacks

Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in power systems heavily depend on the state estimator outputs. While much effort has been given to measurements of false data injection attacks, seldom reported work is found on the broad theme of false data injection on the database of network parameters. State-of-the-art physics-based model solutions correct false data injection on network parameter database considering only available wide-area measurements. In addition, deterministic models are used for correction. In this paper, an overdetermined physics-based parameter false data injection correction model is presented. The overdetermined model uses a parameter database correction Jacobian matrix and a Taylor series expansion approximation. The method further applies the concept of synthetic measurements, which refers to measurements that do not exist in the real-life system. A machine learning linear regression-based model for measurement prediction is integrated in the framework through deriving weights for synthetic measurements creation. Validation of the presented model is performed on the IEEE 118-bus system. Numerical results show that the approximation error is lower than the state-of-the-art, while providing robustness to the correction process. Easy-to-implement model on the classical weighted-least-squares solution, highlights real-life implementation potential aspects.

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Comparative Quality Estimation for Machine Translation Observations on Machine Learning and Features

Prague Bulletin of Mathematical Linguistics ◽

10.1515/pralin-2017-0029 ◽

2017 ◽

Vol 108 (1) ◽

pp. 307-318 ◽

Cited By ~ 1

Author(s):

Eleftherios Avramidis

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Machine Translation ◽

State Of The Art ◽

Linear Method ◽

The State ◽

Quality Estimation ◽

Art Methods ◽

Improved Performance

AbstractA deeper analysis on Comparative Quality Estimation is presented by extending the state-of-the-art methods with adequacy and grammatical features from other Quality Estimation tasks. The previously used linear method, unable to cope with the augmented features, is replaced with a boosting classifier assisted by feature selection. The methods indicated show improved performance for 6 language pairs, when applied on the output from MT systems developed over 7 years. The improved models compete better with reference-aware metrics.Notable conclusions are reached through the examination of the contribution of the features in the models, whereas it is possible to identify common MT errors that are captured by the features. Many grammatical/fluency features have a good contribution, few adequacy features have some contribution, whereas source complexity features are of no use. The importance of many fluency and adequacy features is language-specific.

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Ethics of Machine Learning. A Critical Appraisal of the State of the Art

Konfigurationen der Zeitlichkeit ◽

10.5771/9783748910961-339 ◽

2021 ◽

pp. 339-350

Author(s):

Keyword(s):

Machine Learning ◽

Critical Appraisal ◽

State Of The Art ◽

The State

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Linear Support Vector Machines for Prediction of Student Performance in School-Based Education

Mathematical Problems in Engineering ◽

10.1155/2020/4761468 ◽

2020 ◽

Vol 2020 ◽

pp. 1-7

Author(s):

Nalindren Naicker ◽

Timothy Adeliyi ◽

Jeanette Wing

Keyword(s):

Machine Learning ◽

Support Vector Machines ◽

Student Performance ◽

State Of The Art ◽

Learning Algorithms ◽

The State ◽

Machine Learning Algorithms ◽

Superior Performance ◽

Support Vector ◽

Vector Machines

Educational Data Mining (EDM) is a rich research field in computer science. Tools and techniques in EDM are useful to predict student performance which gives practitioners useful insights to develop appropriate intervention strategies to improve pass rates and increase retention. The performance of the state-of-the-art machine learning classifiers is very much dependent on the task at hand. Investigating support vector machines has been used extensively in classification problems; however, the extant of literature shows a gap in the application of linear support vector machines as a predictor of student performance. The aim of this study was to compare the performance of linear support vector machines with the performance of the state-of-the-art classical machine learning algorithms in order to determine the algorithm that would improve prediction of student performance. In this quantitative study, an experimental research design was used. Experiments were set up using feature selection on a publicly available dataset of 1000 alpha-numeric student records. Linear support vector machines benchmarked with ten categorical machine learning algorithms showed superior performance in predicting student performance. The results of this research showed that features like race, gender, and lunch influence performance in mathematics whilst access to lunch was the primary factor which influences reading and writing performance.

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Binary Classifier Inspired by Quantum Theory

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.330110051 ◽

2019 ◽

Vol 33 ◽

pp. 10051-10052 ◽

Cited By ~ 3

Author(s):

Prayag Tiwari ◽

Massimo Melucci

Keyword(s):

Machine Learning ◽

Quantum Theory ◽

State Of The Art ◽

Substantial Improvement ◽

The State ◽

Food Technology ◽

Binary Classifier ◽

Raw Data ◽

Probability And Statistics ◽

Agricultural Food

Machine Learning (ML) helps us to recognize patterns from raw data. ML is used in numerous domains i.e. biomedical, agricultural, food technology, etc. Despite recent technological advancements, there is still room for substantial improvement in prediction. Current ML models are based on classical theories of probability and statistics, which can now be replaced by Quantum Theory (QT) with the aim of improving the effectiveness of ML. In this paper, we propose the Binary Classifier Inspired by Quantum Theory (BCIQT) model, which outperforms the state of the art classification in terms of recall for every category.

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Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

BMC Bioinformatics ◽

10.1186/1471-2105-13-89 ◽

2012 ◽

Vol 13 (1) ◽

Cited By ~ 55

Author(s):

Rasna R Walia ◽

Cornelia Caragea ◽

Benjamin A Lewis ◽

Fadi Towfic ◽

Michael Terribilini ◽

...

Keyword(s):

Machine Learning ◽

State Of The Art ◽

The State ◽

Interface Residue

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Spam Review Detection:A Systematic Literature Review

10.36227/techrxiv.12951077 ◽

2020 ◽

Author(s):

Muhammad Shoaib Farooq

Keyword(s):

Machine Learning ◽

Decision Making ◽

Literature Review ◽

Systematic Literature Review ◽

State Of The Art ◽

The State ◽

Research Gaps ◽

Art Research ◽

Fake Reviews ◽

Research Studies

In this era of technology, people rely on online posted reviews before buying any product. These reviews are very important for both the consumers and people. Consumers and people use this information for decision making while buying products or investing money in any product. This has inclined the spammers to generate spam or fake reviews so that they can recommend their products and beat the competitors. Spammers have developed many systems to generate the bulk of spam reviews within hours. Many techniques, strategies have been designed and recommended to resolve the issue of spam reviews. In this paper, a complete review of existing techniques and strategies for detecting spam review is discussed. Apart from reviewing the state-of-the-art research studies on spam review detection, a taxonomy on techniques of machine learning for spam review detection has been proposed. Moreover, its focus on research gaps and future recommendations for spam review identification.

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