Related papers: Multi-class classification of Fermi-LAT sources wi…
In this paper, an Extreme Learning Machine (ELM) based technique for Multi-label classification problems is proposed and discussed. In multi-label classification, each of the input data samples belongs to one or more than one class labels.…
In hierarchical multi-label classification, a persistent challenge is enabling model predictions to reach deeper levels of the hierarchy for more detailed or fine-grained classifications. This difficulty partly arises from the natural…
Hierarchical Extreme Multi-Label Classification poses greater difficulties compared to traditional multi-label classification because of the intricate hierarchical connections of labels within a domain-specific taxonomy and the substantial…
In the latest data release from the Fermi $\gamma$-ray Space Telescope (the 4th Fermi LAT 14 yr Catalog, or 4FGL), more than 50% of the Galactic sources are yet to be identified. We observed 13 unidentified Fermi LAT sources with the…
Classification problems are essential statistical tasks that form the foundation of decision-making across various fields, including patient prognosis and treatment strategies for critical conditions. Consequently, evaluating the…
Class imbalance and distributional differences in large datasets present significant challenges for classification tasks machine learning, often leading to biased models and poor predictive performance for minority classes. This work…
Hierarchies are frequently used for the organization of objects. Given a hierarchy of classes, two main approaches are used, to automatically classify new instances: flat classification and cascade classification. Flat classification…
Excess of gamma rays around the Galactic center (GC) observed in the Fermi Large Area Telescope (LAT) data is one of the most intriguing features in the gamma-ray sky. The spherical morphology and the spectral energy distribution with a…
Effective image classification hinges on discerning relevant features from both foreground and background elements, with the foreground typically holding the critical information. While humans adeptly classify images with limited exposure,…
Classifier calibration does not always go hand in hand with the classifier's ability to separate the classes. There are applications where good classifier calibration, i.e. the ability to produce accurate probability estimates, is more…
A common issue for classification in scientific research and industry is the existence of imbalanced classes. When sample sizes of different classes are imbalanced in training data, naively implementing a classification method often leads…
We describe an image-based method that uses two radio criteria, compactness and spectral index, to identify promising pulsar candidates among Fermi Large Area Telescope (LAT) unassociated sources. These criteria are applied to those radio…
We present a catalog of sources of very high energy (E>100 GeV) gamma-rays detected by the Large Area Telescope (LAT) onboard the Fermi Gamma-Ray Space Telescope at Galactic latitudes |b|>=10 degrees. We cross-correlate the directions of…
We introduce a new method for building classification models when we have prior knowledge of how the classes can be arranged in a hierarchy, based on how easily they can be distinguished. The new method uses a Bayesian form of the…
We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a…
A large fraction of the detections to be made by the Gamma-ray Large Area Space Telescope (GLAST) will initially be unidentified. We argue that traditional methodological approaches to identify individuals and/or populations of $\gamma$-ray…
Searching for as yet undetected gamma-ray sources is a major target of the Fermi LAT Collaboration. We present an algorithm capable of identifying such type of sources by non-parametrically clustering the directions of arrival of the…
A primary concern of excessive reuse of test datasets in machine learning is that it can lead to overfitting. Multiclass classification was recently shown to be more resistant to overfitting than binary classification. In an open problem of…
The Fermi Large Area Telescope (LAT) is currently the most important facility for investigating the GeV $\gamma$-ray sky. With Fermi LAT more than three thousand $\gamma$-ray sources have been discovered so far. 1144 ($\sim40\%$) of the…
Deep learning work on musical instrument recognition has generally focused on instrument classes for which we have abundant data. In this work, we exploit hierarchical relationships between instruments in a few-shot learning setup to enable…