29 papers · ranked by Valyu relevance
Trent Henderson, Ben Fulcher
Time series are measured and analyzed across the sciences. One method of quantifying the structure of time series is by calculating a set of summary statistics or 'features', and then representing a time series in terms of its properties as a feature vector. The resulting feature space is interpretable and informative…
Angelos Chatzimparmpas, Rafael M. Martins, Kostiantyn Kucher, Andreas Kerren
'Andreas Kerren'] Abstract—The machine learning (ML) life cycle involves a series of iterative steps, from the effective gathering and preparation of the data—including complex feature engineering processes—to the presentation and improvement of results, with various algorithms to choose from in every step. Feature…
Fábio Mendonça, Sheikh Shanawaz Mostafa, Diogo Freitas, Fernando Morgado-Dias + 2 more
Methodologies for automatic non-rapid eye movement and cyclic alternating pattern analysis were proposed to examine the signal from one electroencephalogram monopolar derivation for the A phase, cyclic alternating pattern cycles, and cyclic alternating pattern rate assessments. A population composed of subjects free of…
Tianping Zhang, Zheyu Zhang, Zhiyuan Fan, Haoyan Luo + 3 more
'Wei Cao' 'Jian Li'] The goal of automated feature generation is to liberate machine learning experts from the laborious task of manual feature generation, which is crucial for improving the learning performance of tabular data. The major challenge in automated feature generation is to efficiently and accurately…
Hamid Karimi-Rouzbahani, Alexandra Woolgar
Neural codes are reflected in complex neural activation patterns. Conventional electroencephalography (EEG) decoding analyses summarize activations by averaging/down-sampling signals within the analysis window. This diminishes informative fine-grained patterns. While previous studies have proposed distinct statistical…
Rahi Jain, Wei Xu
Feature selection (FS) is critical for high dimensional data analysis. Ensemble based feature selection (EFS) is a commonly used approach to develop FS techniques. Rank aggregation (RA) is an essential step of EFS where results from multiple models are pooled to estimate feature importance. However, the literature…
Authors not listed
Traditional and non-classical machine learning models for solid-state structure prediction have predominantly relied on compositional features (derived from properties of constituent elements) to predict the existence of structure and its properties. However, the lack of structural information can be a source of…
Tümay Capraz, Wolfgang Huber
A fundamental step in many analyses of high-dimensional data is dimension reduction. Two basic approaches are introduction of new, synthetic coordinates, and selection of extant features. Advantages of the latter include interpretability, simplicity, transferability and modularity. A common criterion for unsupervised…
Xiangchao Xu, Huijiao Qiao, Zhenfan Xu, Shuya Hu + 1 more
Object-Based Image Analysis (OBIA) generates high-dimensional features that frequently induce the curse of dimensionality, impairing classification efficiency and generalizability in high-resolution remote sensing images. To address these challenges while simultaneously overcoming the limitations of single-criterion…
Beichen Wang, Jiazhang Cai, Luyang Fang, Ping Ma + 1 more
Contemporary neurobehavior research often collects multi-dimensional tensor (MDT) data, consisting of time-series measurements for multiple features from multiple animals subjected to various perturbations. Proper analysis of the MDT data can facilitate the dissection of the underlying neural circuitry driving the…
Suguru Fujita, Yasuaki Karasawa, Ken-ichi Hironaka, Y-h. Taguchi + 1 more
High-throughput omics technologies have enabled the profiling of entire biological systems. For the biological interpretation of such omics data, two analyses, hypothesis- and data-driven analyses including tensor decomposition, have been used. Both analyses have their own advantages and disadvantages and are mutually…
Authors not listed
Solubility is critical in drug discovery and development, as it significantly influences a medication's bioavailability and therapeutic efficacy. Understanding solubility at the early stages of drug discovery is essential for minimizing resource consumption and enhancing the likelihood of clinical success via…
René-Vinicio Sánchez, Jean Carlo Macancela, Luis-Renato Ortega, Diego Cabrera + 3 more
'Diego Cabrera' 'Fausto Pedro García Márquez' 'Mariela Cerrada' 'Jiawei Xiang'] This article presents a comprehensive collection of formulas and calculations for hand-crafted feature extraction of condition monitoring signals. The documented features include 123 for the time domain and 46 for the frequency domain.…
Xiongshi Deng, Min Li, Lei Wang, Qikang Wan
—Feature selection is a preprocessing step which plays a crucial role in the domain of machine learning and data mining. Feature selection methods have been shown to be effective in removing redundant and irrelevant features, improving the learning algorithm's prediction performance. Among the various methods of…
Rahi Jain, Wei Xu
Feature selection (FS) reduces the dimensions of high dimensional data. Among many FS approaches, ensemble-based feature selection (EFS) is one of the commonly used approaches. The rank aggregation (RA) step influences the feature selection of EFS. Currently, the EFS approach relies on using a single RA algorithm to…
Maryam Assafo, Jost Philipp Städter, Tenia Meisel, Peter Langendörfer + 1 more
'Peter Langendörfer' 'Yongbo Li'] Feature selection (FS) represents an essential step for many machine learning-based predictive maintenance (PdM) applications, including various industrial processes, components, and monitoring tasks. The selected features not only serve as inputs to the learning models but also can…
Sangjoon Lee, Clio Chen, Griheydi Garcia, Anton Oliynyk
Materials informatics uses data-driven approaches for the study and discovery of materials. Features or descriptors are the crucial components in generating reliable and accurate machine-learning models. While general data can be acquired through public and commercial sources, features must be tailored for a specific…
Wangyang Ying, Dongjie Wang, Haifeng Chen, Yanjie Fu
Feature selection aims to identify the most pattern-discriminative feature subset. In prior literature, filter (e.g., backward elimination) and embedded (e.g., Lasso) methods have hyperparameters (e.g., top-K, score thresholding) and tie to specific models, thus, hard to generalize; wrapper methods search a feature…
Suvo Banik, Karthik Balasubramanian, Sukriti Manna, Sybil Derrible + 1 more
Identifying key descriptors and understanding important features across different classes of materials are crucial for machine learning (ML) tools to both predict material properties and reveal the physics underlying any process of interest. Traditionally, the predictive modeling of elastic properties of materials is…
Agata Przybyś-Małaczek, Izabella Antoniuk, Karol Szymanowski, Michał Kruk + 2 more
In this article, we present a novel approach to tool condition monitoring in the chipboard milling process using machine learning algorithms. The presented study aims to address the challenges of detecting tool wear and predicting tool failure in real time, which can significantly improve the efficiency and…
Lucas Biaggi, João Paulo Papa, Kelton Augusto Pontara da Costa, Danillo Roberto Pereira + 1 more
'Danillo Roberto Pereira' 'Leandro A. Passos'] Abstract—Identifying anomalies has become one of the primary strategies towards security and protection procedures in computer networks. In this context, machine learning-based methods emerge as an elegant solution to identify such scenarios and learn irrelevant…
Muhammad Rajabinasab, Anton Danholt Lautrup, Tobias Hyrup, Arthur Zimek
'Arthur Zimek'] Abstract. Expressive evaluation metrics are indispensable for informative experiments in all areas, and while several metrics are established in some areas, in others, such as feature selection, only indirect or otherwise limited evaluation metrics are found. In this paper, we propose a novel evaluation…
Fatima Skaka-Čekić, Jasmina Baraković Husić, Almasa Odžak, Mesud Hadžialić + 2 more
Big Data analytics and Artificial Intelligence (AI) technologies have become the focus of recent research due to the large amount of data. Dimensionality reduction techniques are recognized as an important step in these analyses. The multidimensional nature of Quality of Experience (QoE) is based on a set of Influence…
Soukhin Das, G.R. Mangun, Mingzhou Ding
Perceptual expertise and attention are two important factors that enable superior object recognition and task performance. While expertise enhances knowledge and provides a holistic understanding of the environment, attention allows us to selectively focus on task-related information and suppress distraction. It has…
Authors not listed
The global drive towards net-zero has accelerated the adoption of carbon fibre reinforced polymers (CFRP) for lightweight structures in various sectors such as aerospace, automotive, energy and biomedical. Mechanical machining of CFRP is often necessary to meet dimensional or assembly-related requirements. However…
Mohammed Abdalsalam, Chunlin Li, Abdelghani Dahou, Natalia Kryvinska + 1 more
The world faces the ongoing challenge of terrorism and extremism, which threaten the stability of nations, the security of their citizens, and the integrity of political, economic, and social systems. Given the complexity and multifaceted nature of this phenomenon, combating it requires a collective effort, with…
Anthony Onwuli, Keith T. Butler, Aron Walsh
High-dimensional representations of the elements have become common within the field of materials informatics to build useful, structure-agnostic models for the chemistry of materials. However, the characteristics of elements change when they adopt a given oxidation state, with distinct structural preferences and…
Raheel Hammad, Sownyak Mondal
The Gibbs free energy of an inorganic material represents its maximum reversible work potential under constant temperature and pressure. Its calculation is crucial for understanding material stability, phase transitions, and chemical reactions, thus guiding optimization for diverse applications like catalysis and…
Tarun Khajuria, Kadi Tulver, Jaan Aru
Human vision is not merely a passive process of interpreting sensory input but can also function as a problem-solving process incorporating generative mechanisms to interpret ambiguous or noisy data. This synergy between the generative and discriminative components, often described as analysis-by-synthesis, enables…