Pulsars Detection by Machine Learning with Very Few Features

• URL: http://arxiv.org/abs/2002.08519v1
• Date: Thu, 20 Feb 2020 01:26:42 GMT
• Title: Pulsars Detection by Machine Learning with Very Few Features
• Authors: Haitao Lin, Xiangru Li, Ziying Luo
• Abstract summary: It is an active topic to investigate the schemes based on machine learning (ML) methods for detecting pulsars. To improve the detection performance, input features into an ML model should be investigated specifically.
• Score: 5.598468451834693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is an active topic to investigate the schemes based on machine learning (ML) methods for detecting pulsars as the data volume growing exponentially in modern surveys. To improve the detection performance, input features into an ML model should be investigated specifically. In the existing pulsar detection researches based on ML methods, there are mainly two kinds of feature designs: the empirical features and statistical features. Due to the combinational effects from multiple features, however, there exist some redundancies and even irrelevant components in the available features, which can reduce the accuracy of a pulsar detection model. Therefore, it is essential to select a subset of relevant features from a set of available candidate features and known as {\itshape feature selection.} In this work, two feature selection algorithms ----\textit{Grid Search} (GS) and \textit{Recursive Feature Elimination} (RFE)---- are proposed to improve the detection performance by removing the redundant and irrelevant features. The algorithms were evaluated on the Southern High Time Resolution University survey (HTRU-S) with five pulsar detection models. The experimental results verify the effectiveness and efficiency of our proposed feature selection algorithms. By the GS, a model with only two features reach a recall rate as high as 99\% and a false positive rate (FPR) as low as 0.65\%; By the RFE, another model with only three features achieves a recall rate 99\% and an FPR of 0.16\% in pulsar candidates classification. Furthermore, this work investigated the number of features required as well as the misclassified pulsars by our models.

Related papers

• Detection-Rate-Emphasized Multi-objective Evolutionary Feature Selection for Network Intrusion Detection [21.104686670216445]
  We propose DR-MOFS to model the feature selection problem in network intrusion detection as a three-objective optimization problem. In most cases, the proposed method can outperform previous methods, i.e., lead to fewer features, higher accuracy and detection rate.
  arXiv  Detail & Related papers  (2024-06-13T14:42:17Z)
• Temporal Stamp Classifier: Classifying Short Sequences of Astronomical Alerts [0.6144680854063939]
  We propose a deep learning-based classification model of astronomical objects using alerts reported by the Zwicky Transient Facility (ZTF) survey. The model takes as inputs sequences of stamp images and metadata contained in each alert, as well as features from the All-WISE catalog. The proposed model is able to discriminate between three classes of astronomical objects, with an accuracy of approximately 98% in the test set.
  arXiv  Detail & Related papers  (2024-05-23T21:49:32Z)
• The object detection model uses combined extraction with KNN and RF classification [0.0]
  This study contributes to the field of object detection with a new approach combining GLCM and LBP as feature vectors as well as VE for classification. System testing used a dataset of 4,437 2D images, the results for KNN accuracy were 92.7% and F1-score 92.5%, while RF performance was lower.
  arXiv  Detail & Related papers  (2024-05-09T05:21:42Z)
• A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning [131.2910403490434]
  Data scientists typically collect as many features as possible into their datasets, and even engineer new features from existing ones. Existing benchmarks for tabular feature selection consider classical downstream models, toy synthetic datasets, or do not evaluate feature selectors on the basis of downstream performance. We construct a challenging feature selection benchmark evaluated on downstream neural networks including transformers. We also propose an input-gradient-based analogue of Lasso for neural networks that outperforms classical feature selection methods on challenging problems.
  arXiv  Detail & Related papers  (2023-11-10T05:26:10Z)
• Causal Feature Selection via Transfer Entropy [59.999594949050596]
  Causal discovery aims to identify causal relationships between features with observational data. We introduce a new causal feature selection approach that relies on the forward and backward feature selection procedures. We provide theoretical guarantees on the regression and classification errors for both the exact and the finite-sample cases.
  arXiv  Detail & Related papers  (2023-10-17T08:04:45Z)
• A Hybrid Feature Selection and Construction Method for Detection of Wind Turbine Generator Heating Faults [0.0]
  In this paper, a feature selection and construction approach is presented for the detection of wind turbine generator heating faults. Original features directly collected from the data collection system consist of wind characteristics, operational data, temperature measurements and status information. New features were created in the feature construction step to obtain information that can be more powerful indications of the faults. The results show that, the proposed approach contributes to the fault detection system to be more reliable especially in terms of reducing the number of false fault alarms.
  arXiv  Detail & Related papers  (2023-06-15T20:37:30Z)
• DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature [143.5381108333212]
  We show that text sampled from an large language model tends to occupy negative curvature regions of the model's log probability function. We then define a new curvature-based criterion for judging if a passage is generated from a given LLM. We find DetectGPT is more discriminative than existing zero-shot methods for model sample detection.
  arXiv  Detail & Related papers  (2023-01-26T18:44:06Z)
• ReDFeat: Recoupling Detection and Description for Multimodal Feature Learning [51.07496081296863]
  We recouple independent constraints of detection and description of multimodal feature learning with a mutual weighting strategy. We propose a detector that possesses a large receptive field and is equipped with learnable non-maximum suppression layers. We build a benchmark that contains cross visible, infrared, near-infrared and synthetic aperture radar image pairs for evaluating the performance of features in feature matching and image registration tasks.
  arXiv  Detail & Related papers  (2022-05-16T04:24:22Z)
• Generalizing Face Forgery Detection with High-frequency Features [63.33397573649408]
  Current CNN-based detectors tend to overfit to method-specific color textures and thus fail to generalize. We propose to utilize the high-frequency noises for face forgery detection. The first is the multi-scale high-frequency feature extraction module that extracts high-frequency noises at multiple scales. The second is the residual-guided spatial attention module that guides the low-level RGB feature extractor to concentrate more on forgery traces from a new perspective.
  arXiv  Detail & Related papers  (2021-03-23T08:19:21Z)
• Feature Selection for Huge Data via Minipatch Learning [0.0]
  We propose Stable Minipatch Selection (STAMPS) and Adaptive STAMPS. STAMPS are meta-algorithms that build ensembles of selection events of base feature selectors trained on tiny, (ly-adaptive) random subsets of both the observations and features of the data. Our approaches are general and can be employed with a variety of existing feature selection strategies and machine learning techniques.
  arXiv  Detail & Related papers  (2020-10-16T17:41:08Z)
• Condensing Two-stage Detection with Automatic Object Key Part Discovery [87.1034745775229]
  Two-stage object detectors generally require excessively large models for their detection heads to achieve high accuracy. We propose that the model parameters of two-stage detection heads can be condensed and reduced by concentrating on object key parts. Our proposed technique consistently maintains original performance while waiving around 50% of the model parameters of common two-stage detection heads.
  arXiv  Detail & Related papers  (2020-06-10T01:20:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.