A Novel Approach for Optimum-Path Forest Classification Using Fuzzy Logic

• URL: http://arxiv.org/abs/2204.06635v1
• Date: Wed, 13 Apr 2022 20:55:30 GMT
• Title: A Novel Approach for Optimum-Path Forest Classification Using Fuzzy Logic
• Authors: Renato W. R. de Souza, Jo\~ao V. C. de Oliveira, Leandro A. Passos, Weiping Ding, Jo\~ao P. Papa, and Victor Hugo C. de Albuquerque
• Abstract summary: Fuzzy Optimum-Path Forest is an improved version of the standard OPF classifier. It learns the samples' membership in an unsupervised fashion, which are further incorporated during supervised training. Experiments conducted over twelve public datasets highlight the robustness of the proposed approach.
• Score: 13.313728527879306
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the past decades, fuzzy logic has played an essential role in many research areas. Alongside, graph-based pattern recognition has shown to be of great importance due to its flexibility in partitioning the feature space using the background from graph theory. Some years ago, a new framework for both supervised, semi-supervised, and unsupervised learning named Optimum-Path Forest (OPF) was proposed with competitive results in several applications, besides comprising a low computational burden. In this paper, we propose the Fuzzy Optimum-Path Forest, an improved version of the standard OPF classifier that learns the samples' membership in an unsupervised fashion, which are further incorporated during supervised training. Such information is used to identify the most relevant training samples, thus improving the classification step. Experiments conducted over twelve public datasets highlight the robustness of the proposed approach, which behaves similarly to standard OPF in worst-case scenarios.

Related papers

• A New Random Forest Ensemble of Intuitionistic Fuzzy Decision Trees [5.831659043074847]
  We propose a new random forest ensemble of intuitionistic fuzzy decision trees (IFDT) The proposed method enjoys the power of the randomness from bootstrapped sampling and feature selection. This study is the first to propose a random forest ensemble based on the intuitionistic fuzzy theory.
  arXiv  Detail & Related papers  (2024-03-12T06:52:24Z)
• On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training [72.8087629914444]
  We study the impact of the trade-off between the intra-class diversity (the number of samples per class) and the inter-class diversity (the number of classes) of a supervised pre-training dataset. With the size of the pre-training dataset fixed, the best downstream performance comes with a balance on the intra-/inter-class diversity.
  arXiv  Detail & Related papers  (2023-05-20T16:23:50Z)
• The Cascaded Forward Algorithm for Neural Network Training [61.06444586991505]
  We propose a new learning framework for neural networks, namely Cascaded Forward (CaFo) algorithm, which does not rely on BP optimization as that in FF. Unlike FF, our framework directly outputs label distributions at each cascaded block, which does not require generation of additional negative samples. In our framework each block can be trained independently, so it can be easily deployed into parallel acceleration systems.
  arXiv  Detail & Related papers  (2023-03-17T02:01:11Z)
• Open World Classification with Adaptive Negative Samples [89.2422451410507]
  Open world classification is a task in natural language processing with key practical relevance and impact. We propose an approach based on underlineadaptive underlinesamples (ANS) designed to generate effective synthetic open category samples in the training stage. ANS achieves significant improvements over state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-09T21:12:46Z)
• Proposal Distribution Calibration for Few-Shot Object Detection [65.19808035019031]
  In few-shot object detection (FSOD), the two-step training paradigm is widely adopted to mitigate the severe sample imbalance. Unfortunately, the extreme data scarcity aggravates the proposal distribution bias, hindering the RoI head from evolving toward novel classes. We introduce a simple yet effective proposal distribution calibration (PDC) approach to neatly enhance the localization and classification abilities of the RoI head.
  arXiv  Detail & Related papers  (2022-12-15T05:09:11Z)
• Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport [78.9167477093745]
  We propose a novel distribution calibration method by learning the adaptive weight matrix between novel samples and base classes. Experimental results on standard benchmarks demonstrate that our proposed plug-and-play model outperforms competing approaches.
  arXiv  Detail & Related papers  (2022-10-09T02:32:57Z)
• Bucketized Active Sampling for Learning ACOPF [15.509961352249434]
  This paper proposes Bucketized Active Sampling (BAS) to meet the requirements of market-clearing applications. BAS partitions the input domain into buckets and uses an acquisition function to determine where to sample next. BAS also relies on an adaptive learning rate that increases and decreases over time.
  arXiv  Detail & Related papers  (2022-08-16T02:04:17Z)
• Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection [8.492340530784697]
  We show that finetune-free iFSD can be highly effective when a large number of base categories with abundant data are available for meta-training. We benchmark our model on both COCO and LVIS, reporting as high as $17%$ AP on the long-tail rare classes on LVIS.
  arXiv  Detail & Related papers  (2022-03-25T20:39:00Z)
• Learning What Not to Segment: A New Perspective on Few-Shot Segmentation [63.910211095033596]
  Recently few-shot segmentation (FSS) has been extensively developed. This paper proposes a fresh and straightforward insight to alleviate the problem. In light of the unique nature of the proposed approach, we also extend it to a more realistic but challenging setting.
  arXiv  Detail & Related papers  (2022-03-15T03:08:27Z)
• Comparative Study Between Distance Measures On Supervised Optimum-Path Forest Classification [0.0]
  Optimum-Path Forest (OPF) uses a graph-based methodology and a distance measure to create arcs between nodes and hence sets of trees. This work proposes a comparative study over a wide range of distance measures applied to the supervised Optimum-Path Forest classification.
  arXiv  Detail & Related papers  (2022-02-08T13:34:09Z)

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.