Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image Classification

• URL: http://arxiv.org/abs/2310.17250v2
• Date: Tue, 04 Feb 2025 07:30:55 GMT
• Title: Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image Classification
• Authors: Anh T. Hoang, Zsolt J. Viharos,
• Abstract summary: MICS-EFS is a model input-output configuration search with embedded feature selection. It delivers an average accuracy improvement of 1.5% over baseline models. It reduces feature dimensionality to just 2-5% of the original data, significantly enhancing computational efficiency.
• Score: 0.0
• License:
• Abstract: Machine learning is a powerful tool for extracting valuable information and making various predictions from diverse datasets. Traditional machine learning algorithms rely on well-defined input and output variables; however, there are scenarios where the separation between the input and output variables and the underlying, associated input and output layers of the model are unknown. Feature Selection (FS) and Neural Architecture Search (NAS) have emerged as promising solutions in such scenarios. This paper proposes MICS-EFS, a Model Input-Output Configuration Search with Embedded Feature Selection. The methodology explores internal dependencies in the complete input parameter space for classification tasks involving both 1D sensor time-series and 2D image data. MICS-EFS employs a modified encoder-decoder model and the Sequential Forward Search (SFS) algorithm, combining input-output configuration search with embedded feature selection. Experimental results demonstrate the superior performance of MICS-EFS compared to other FS algorithms. Across all tested datasets, MICS-EFS delivered an average accuracy improvement of 1.5% over baseline models, with the accuracy gains ranging from 0.5% to 5.9%. Moreover, the algorithm reduced feature dimensionality to just 2-5% of the original data, significantly enhancing computational efficiency. These results highlight the potential of MICS-EFS to improve model accuracy and efficiency in various machine learning tasks. Furthermore, the proposed method has been validated in a real-world industrial application focused on machining processes, underscoring its effectiveness and practicality in addressing complex input-output challenges.

Related papers

• Efficient Network Traffic Feature Sets for IoT Intrusion Detection [0.0]
  This work evaluates the feature sets provided by a combination of different feature selection methods, namely Information Gain, Chi-Squared Test, Recursive Feature Elimination, Mean Absolute Deviation, and Dispersion Ratio, in multiple IoT network datasets. The influence of the smaller feature sets on both the classification performance and the training time of ML models is compared, with the aim of increasing the computational efficiency of IoT intrusion detection.
  arXiv  Detail & Related papers  (2024-06-12T09:51:29Z)
• Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems [30.16714420093091]
  This paper first introduces a novel neural network architecture called Multiple-Input Auto-Encoder (MIAE) MIAE consists of multiple sub-encoders that can process inputs from different sources with different characteristics. To distil and retain more relevant features but remove less important/redundant ones during the training process, we further design and embed a feature selection layer. This layer learns the importance of features in the representation vector, facilitating the selection of informative features from the representation vector.
  arXiv  Detail & Related papers  (2024-03-22T03:54:04Z)
• AFS-BM: Enhancing Model Performance through Adaptive Feature Selection with Binary Masking [0.0]
  We introduce the "Adaptive Feature Selection with Binary Masking" (AFS-BM) We do the joint optimization and binary masking to continuously adapt the set of features and model parameters during the training process. Our results show that AFS-BM makes significant improvement in terms of accuracy and requires significantly less computational complexity.
  arXiv  Detail & Related papers  (2024-01-20T15:09:41Z)
• A Weighted K-Center Algorithm for Data Subset Selection [70.49696246526199]
  Subset selection is a fundamental problem that can play a key role in identifying smaller portions of the training data. We develop a novel factor 3-approximation algorithm to compute subsets based on the weighted sum of both k-center and uncertainty sampling objective functions.
  arXiv  Detail & Related papers  (2023-12-17T04:41:07Z)
• Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person Search [60.626459715780605]
  Given a descriptive text query, text-based person search aims to retrieve the best-matched target person from an image gallery. Such a cross-modal retrieval task is quite challenging due to significant modality gap, fine-grained differences and insufficiency of annotated data. In this paper, we propose a simple yet effective dual Transformer model for text-based person search.
  arXiv  Detail & Related papers  (2023-11-15T16:26:49Z)
• Transformer-based Context Condensation for Boosting Feature Pyramids in Object Detection [77.50110439560152]
  Current object detectors typically have a feature pyramid (FP) module for multi-level feature fusion (MFF) We propose a novel and efficient context modeling mechanism that can help existing FPs deliver better MFF results. In particular, we introduce a novel insight that comprehensive contexts can be decomposed and condensed into two types of representations for higher efficiency.
  arXiv  Detail & Related papers  (2022-07-14T01:45:03Z)
• Concurrent Neural Tree and Data Preprocessing AutoML for Image Classification [0.5735035463793008]
  Current state-of-the-art (SOTA) methods do not include traditional methods for manipulating input data as part of the algorithmic search space. We adapt the Evolutionary Multi-objective Algorithm Design Engine (EMADE), a multi-objective evolutionary search framework for traditional machine learning methods, to perform neural architecture search. We show that including these methods as part of the search space shows potential to provide benefits to performance on the CIFAR-10 image classification benchmark dataset.
  arXiv  Detail & Related papers  (2022-05-25T20:03:09Z)
• Compactness Score: A Fast Filter Method for Unsupervised Feature Selection [66.84571085643928]
  We propose a fast unsupervised feature selection method, named as, Compactness Score (CSUFS) to select desired features. Our proposed algorithm seems to be more accurate and efficient compared with existing algorithms.
  arXiv  Detail & Related papers  (2022-01-31T13:01:37Z)
• RoMA: Robust Model Adaptation for Offline Model-based Optimization [115.02677045518692]
  We consider the problem of searching an input maximizing a black-box objective function given a static dataset of input-output queries. A popular approach to solving this problem is maintaining a proxy model that approximates the true objective function. Here, the main challenge is how to avoid adversarially optimized inputs during the search.
  arXiv  Detail & Related papers  (2021-10-27T05:37:12Z)
• ASFD: Automatic and Scalable Face Detector [129.82350993748258]
  We propose a novel Automatic and Scalable Face Detector (ASFD) ASFD is based on a combination of neural architecture search techniques as well as a new loss design. Our ASFD-D6 outperforms the prior strong competitors, and our lightweight ASFD-D0 runs at more than 120 FPS with Mobilenet for VGA-resolution images.
  arXiv  Detail & Related papers  (2020-03-25T06:00:47Z)
• ARDA: Automatic Relational Data Augmentation for Machine Learning [23.570173866941612]
  We present system, an end-to-end system that takes as input a dataset and a data repository, and outputs an augmented data set. Our system has two distinct components: (1) a framework to search and join data with the input data, based on various attributes of the input, and (2) an efficient feature selection algorithm that prunes out noisy or irrelevant features from the resulting join.
  arXiv  Detail & Related papers  (2020-03-21T21:55:22Z)

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.