Wrapper Feature Selection Algorithm for the Optimization of an Indicator System of Patent Value Assessment

• URL: http://arxiv.org/abs/2001.08371v1
• Date: Tue, 21 Jan 2020 06:04:42 GMT
• Title: Wrapper Feature Selection Algorithm for the Optimization of an Indicator System of Patent Value Assessment
• Authors: Yihui Qiu, Chiyu Zhang
• Abstract summary: The limitations of previous research on patent value assessment were analyzed. A wrapper-mode feature selection algorithm that is based on classifier prediction accuracy was developed.
• Score: 1.52292571922932
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Effective patent value assessment provides decision support for patent transection and promotes the practical application of patent technology. The limitations of previous research on patent value assessment were analyzed in this work, and a wrapper-mode feature selection algorithm that is based on classifier prediction accuracy was developed. Verification experiments on multiple UCI standard datasets indicated that the algorithm effectively reduced the size of the feature set and significantly enhanced the prediction accuracy of the classifier. When the algorithm was utilized to establish an indicator system of patent value assessment, the size of the system was reduced, and the generalization performance of the classifier was enhanced. Sequential forward selection was applied to further reduce the size of the indicator set and generate an optimal indicator system of patent value assessment.

Related papers

• Direct Preference Optimization with Rating Information: Practical Algorithms and Provable Gains [67.71020482405343]
  We study how to design algorithms that can leverage additional information in the form of rating gap.<n>We present new algorithms that can achieve faster statistical rates than DPO in presence of accurate rating gap information.
  arXiv  Detail & Related papers  (2026-01-31T08:38:21Z)
• Buffered AUC maximization for scoring systems via mixed-integer optimization [1.8224668251608893]
  A scoring system is a linear classifier composed of a small number of explanatory variables, each assigned a small integer coefficient.<n>Several previous studies have used mixed-integer optimization (MIO) techniques to develop scoring systems for binary classification.<n>This research contributes to the advancement of MIO techniques for developing highly interpretable classification models.
  arXiv  Detail & Related papers  (2026-01-09T05:52:13Z)
• Principled Algorithms for Optimizing Generalized Metrics in Binary Classification [53.604375124674796]
  We introduce principled algorithms for optimizing generalized metrics, supported by $H$-consistency and finite-sample generalization bounds.<n>Our approach reformulates metric optimization as a generalized cost-sensitive learning problem.<n>We develop new algorithms, METRO, with strong theoretical performance guarantees.
  arXiv  Detail & Related papers  (2025-12-29T01:33:42Z)
• Regularized GLISp for sensor-guided human-in-the-loop optimization [0.769971486557519]
  We introduce a sensor-guided regularized extension of GLISp that integrates measurable descriptors into the preference-learning loop.<n>This injects grey-box structure, combining subjective feedback with quantitative sensor information.<n> Numerical evaluations on an analytical benchmark and on a human-in-the-loop vehicle suspension tuning task show faster convergence and superior final solutions.
  arXiv  Detail & Related papers  (2025-11-06T19:00:56Z)
• Research on Evaluation Methods for Patent Novelty Search Systems and Empirical Analysis [1.5611734619214201]
  We propose a comprehensive evaluation methodology that builds high-quality, reproducible datasets from examiner citations and X-type citations extracted from technically consistent family patents.<n>Experiments show the method effectively exposes performance differences across scenarios and offers actionable evidence for system improvement.<n>The framework is scalable and practical, providing a useful reference for development and optimization of patent novelty search systems.
  arXiv  Detail & Related papers  (2025-08-25T08:24:04Z)
• Application and Optimization of Large Models Based on Prompt Tuning for Fact-Check-Worthiness Estimation [9.577859600179083]
  We construct a model for fact-check-worthiness estimation at the methodological level using prompt tuning. We establish in-context learning and leverage prompt tuning technology to improve the accuracy of determining whether claims have fact-check-worthiness.
  arXiv  Detail & Related papers  (2025-04-25T06:16:41Z)
• Scoring Verifiers: Evaluating Synthetic Verification for Code and Reasoning [59.25951947621526]
  We propose an approach which can transform existing coding benchmarks into scoring and ranking datasets to evaluate the effectiveness of synthetic verifiers. We release four new benchmarks (HE-R, HE-R+, MBPP-R, and MBPP-R+), and analyzed synthetic verification methods with standard, reasoning-based, and reward-based LLMs. Our experiments show that reasoning can significantly improve test case generation and that scaling the number of test cases enhances the verification accuracy.
  arXiv  Detail & Related papers  (2025-02-19T15:32:11Z)
• A Hybrid Framework for Statistical Feature Selection and Image-Based Noise-Defect Detection [55.2480439325792]
  This paper presents a hybrid framework that integrates both statistical feature selection and classification techniques to improve defect detection accuracy. We present around 55 distinguished features that are extracted from industrial images, which are then analyzed using statistical methods. By integrating these methods with flexible machine learning applications, the proposed framework improves detection accuracy and reduces false positives and misclassifications.
  arXiv  Detail & Related papers  (2024-12-11T22:12:21Z)
• DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection [52.100335904875614]
  We present a novel prompt tuning approach, namely, Decomposed Context Optimization (DeCoOp), which introduces new-class detectors and sub-classifiers to further enhance the base-class and new-class discriminability. Experimental results on 11 benchmark datasets validate the effectiveness of DePT and demonstrate that DeCoOp outperforms current state-of-the-art methods, providing a significant 2% average accuracy improvement.
  arXiv  Detail & Related papers  (2024-06-01T07:46:42Z)
• SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms [10.815433358168082]
  We propose a unified framework designed to establish standardization in the validation of EEG-based seizure detection algorithms. Based on existing guidelines and recommendations, the framework introduces a set of recommendations and standards related to datasets, file formats, EEG data input content, seizure annotation input and output, cross-validation strategies, and performance metrics. We also propose the 10-20 seizure detection benchmark, a machine-learning benchmark based on public datasets converted to a standardized format.
  arXiv  Detail & Related papers  (2024-02-20T13:38:04Z)
• Improved Algorithms for Neural Active Learning [74.89097665112621]
  We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting. We introduce two regret metrics by minimizing the population loss that are more suitable in active learning than the one used in state-of-the-art (SOTA) related work.
  arXiv  Detail & Related papers  (2022-10-02T05:03:38Z)
• A Tent L\'evy Flying Sparrow Search Algorithm for Feature Selection: A COVID-19 Case Study [1.6436293069942312]
  The "Curse of Dimensionality" induced by the rapid development of information science might have a negative impact when dealing with big datasets. We propose a variant of the sparrow search algorithm (SSA), called Tent L'evy flying sparrow search algorithm (TFSSA) TFSSA is used to select the best subset of features in the packing pattern for classification purposes.
  arXiv  Detail & Related papers  (2022-09-20T15:12:10Z)
• Large-Scale Sequential Learning for Recommender and Engineering Systems [91.3755431537592]
  In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions. For the former, we propose novel algorithm called SAROS that take into account both kinds of feedback for learning over the sequence of interactions. The proposed idea of taking into account the neighbour lines shows statistically significant results in comparison with the initial approach for faults detection in power grid.
  arXiv  Detail & Related papers  (2022-05-13T21:09:41Z)
• A Survey on Sentence Embedding Models Performance for Patent Analysis [0.0]
  We propose a standard library and dataset for assessing the accuracy of embeddings models based on PatentSBERTa approach. Results show PatentSBERTa, Bert-for-patents, and TF-IDF Weighted Word Embeddings have the best accuracy for computing sentence embeddings at the subclass level.
  arXiv  Detail & Related papers  (2022-04-28T12:04:42Z)
• Investigation of Different Calibration Methods for Deep Speaker Embedding based Verification Systems [66.61691401921296]
  This paper presents an investigation over several methods of score calibration for deep speaker embedding extractors. An additional focus of this research is to estimate the impact of score normalization on the calibration performance of the system.
  arXiv  Detail & Related papers  (2022-03-28T21:22:22Z)
• Adaptive Template Enhancement for Improved Person Recognition using Small Datasets [0.9668407688201358]
  A novel instance-based method for the classification of electroencephalography (EEG) signals is presented and evaluated in this paper. The proposed adaptive template enhancement mechanism transforms the feature-level instances by treating each feature dimension separately. The proposed approach demonstrates significantly improved classification accuracy in both identification and verification scenarios.
  arXiv  Detail & Related papers  (2022-01-03T10:14:38Z)
• AP-Loss for Accurate One-Stage Object Detection [49.13608882885456]
  One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously. The former suffers much from extreme foreground-background imbalance due to the large number of anchors. This paper proposes a novel framework to replace the classification task in one-stage detectors with a ranking task.
  arXiv  Detail & Related papers  (2020-08-17T13:22:01Z)
• Stochastic batch size for adaptive regularization in deep network optimization [63.68104397173262]
  We propose a first-order optimization algorithm incorporating adaptive regularization applicable to machine learning problems in deep learning framework. We empirically demonstrate the effectiveness of our algorithm using an image classification task based on conventional network models applied to commonly used benchmark datasets.
  arXiv  Detail & Related papers  (2020-04-14T07:54:53Z)
• Surrogate-assisted performance prediction for data-driven knowledge discovery algorithms: application to evolutionary modeling of clinical pathways [0.0]
  The paper proposes and investigates an approach for surrogate-assisted performance prediction of data-driven knowledge discovery algorithms. The approach is based on the identification of surrogate models for prediction of the target algorithm's quality and performance.
  arXiv  Detail & Related papers  (2020-04-02T16:49:43Z)

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.