Related papers: Band Relevance Factor (BRF): a novel automatic fre…
Fast Radio Bursts (FRBs) are millisecond-duration radio transients of extragalactic origin, exhibiting a wide range of physical and observational properties. Distinguishing between repeating and non-repeating FRBs remains a key challenge in…
A new approach called ABRF (the attention-based random forest) and its modifications for applying the attention mechanism to the random forest (RF) for regression and classification are proposed. The main idea behind the proposed ABRF…
Fast Radio Burst (FRB) is an extremely energetic cosmic phenomenon of short duration. Discovered only recently and with its origin still unknown, FRBs have already started to play a significant role in studying the distribution and…
Kernel-based classification methods, particularly the support vector machine (SVM), are among the most common algorithms for hyperspectral data classification. The Radial Basis function (RBF) kernel has earned great popularity in…
Rolling bearings are critical components in rotating machinery, and their faults can cause severe damage. Early detection of abnormalities is crucial to prevent catastrophic accidents. Traditional and intelligent methods have been used to…
Click-through rate (CTR) prediction plays important role in personalized advertising and recommender systems. Though many models have been proposed such as FM, FFM and DeepFM in recent years, feature engineering is still a very important…
The effectiveness of machine learning models is significantly affected by the size of the dataset and the quality of features as redundant and irrelevant features can radically degrade the performance. This paper proposes IGRF-RFE: a hybrid…
A growing number of wind turbines are equipped with vibration measurement systems to enable a close monitoring and early detection of developing fault conditions. The vibration measurements are analyzed to continuously assess the component…
We propose a feature screening method that integrates both feature-feature and feature-target relationships. Inactive features are identified via a penalized minimum Redundancy Maximum Relevance (mRMR) procedure, which is the continuous…
As more and more search traffic comes from mobile phones, intelligent assistants, and smart-home devices, new challenges (e.g., limited presentation space) and opportunities come up in information retrieval. Previously, an effective…
Fast Radio Bursts (FRBs), a class of millisecond-scale, highly energetic phenomena with unknown progenitors and radiation mechanisms, require proper statistical analysis as a key method for uncovering their mysteries. In this research, we…
Magnetic Resonance Fingerprinting (MRF) is an emerging technology with the potential to revolutionize radiology and medical diagnostics. In comparison to traditional magnetic resonance imaging (MRI), MRF enables the rapid, simultaneous,…
Real-time, low-cost, and wireless mechanical vibration monitoring is necessary for industrial applications to track the operation status of equipment, environmental applications to proactively predict natural disasters, as well as…
Hyperspectral bands offer rich spectral and spatial information; however, their high dimensionality poses challenges for efficient processing. Band selection (BS) methods aim to extract a smaller subset of bands to reduce spectral…
The use of variable selection methods is particularly appealing in statistical problems with functional data. The obvious general criterion for variable selection is to choose the `most representative' or `most relevant' variables. However,…
In the domain of rotating machinery, bearings are vulnerable to different mechanical faults, including ball, inner, and outer race faults. Various techniques can be used in condition-based monitoring, from classical signal analysis to deep…
Deep learning-based radio frequency fingerprinting (RFFP) has become an enabling physical-layer security technology, allowing device identification and authentication through received RF signals. This technology, however, faces significant…
This paper introduces Spectral Fault Receptive Fields (SFRFs), a biologically inspired technique for degradation state assessment in bearing fault diagnosis and remaining useful life (RUL) estimation. Drawing on the center-surround…
Faster likelihood evaluation enhances the efficiency of gravitational wave signal analysis. We present Mode-by-mode Relative Binning (MRB), a new method designed for obtaining fast and accurate likelihoods for advanced waveform models that…
In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical. The performance metric used to compare different algorithms must also reflect the concerns of users in the application domain…