2,438 research outputs found
Fuzzy-rough-learn 0.1 : a Python library for machine learning with fuzzy rough sets
We present fuzzy-rough-learn, the first Python library of fuzzy rough set machine learning algorithms. It contains three algorithms previously implemented in R and Java, as well as two new algorithms from the recent literature. We briefly discuss the use cases of fuzzy-rough-learn and the design philosophy guiding its development, before providing an overview of the included algorithms and their parameters
ML-k’sNN: Label Dependent k Values for Multi-Label k-Nearest Neighbor Rule
Multi-label classification as a data mining task has recently attracted increasing interest from researchers. Many current data mining applications address problems with instances that belong to more than one category. These problems require the development of new, efficient methods. Multi-label k-nearest neighbors rule, ML-kNN, is among the best-performing methods for multi-label problems. Current methods use a unique k value for all labels, as in the single-label method. However, the distributions of the labels are frequently very different. In such scenarios, a unique k value for the labels might be suboptimal. In this paper, we propose a novel approach in which each label is predicted with a different value of k. Obtaining the best k for each label is stated as an optimization problem. Three different algorithms are proposed for this task, depending on which multi-label metric is the target of our optimization process. In a large set of 40 real-world multi-label problems, our approach improves the results of two different tested ML-kNN implementations
A multi-label approach for diagnosis problems in energy systems using LAMDA algorithm
2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 18-23 July 2022, Italia.In this paper, we propose a supervised multilabel algorithm called Learning Algorithm for Multivariate Data Analysis for Multilabel Classification (LAMDA-ML). This algorithm is based on the algorithms of the LAMDA family, in particular, on the LAMDA-HAD (Higher Adequacy Grade) algorithm. Unlike previous algorithms in a multi-label context, LAMDA-ML is based on the Global Adequacy Degree (GAD) of an individual in multiple classes. In our proposal, we define a membership threshold (Gt), such that for all GAD values above this threshold, it implies that an individual will be assigned to the respective classes. For the evaluation of the performance of this proposal, a solar power generation dataset is used, with very encouraging results according to several metrics in the context of multiple labels.European CommissionAgencia Estatal de InvestigaciĂłnJunta de Comunidades de Castilla-La Manch
IMAGE PROCESSING, SEGMENTATION AND MACHINE LEARNING MODELS TO CLASSIFY AND DELINEATE TUMOR VOLUMES TO SUPPORT MEDICAL DECISION
Techniques for processing and analysing images and medical data have become
the main’s translational applications and researches in clinical and pre-clinical
environments. The advantages of these techniques are the improvement of diagnosis
accuracy and the assessment of treatment response by means of quantitative biomarkers
in an efficient way. In the era of the personalized medicine, an early and
efficacy prediction of therapy response in patients is still a critical issue.
In radiation therapy planning, Magnetic Resonance Imaging (MRI) provides high
quality detailed images and excellent soft-tissue contrast, while Computerized
Tomography (CT) images provides attenuation maps and very good hard-tissue
contrast. In this context, Positron Emission Tomography (PET) is a non-invasive
imaging technique which has the advantage, over morphological imaging techniques,
of providing functional information about the patient’s disease.
In the last few years, several criteria to assess therapy response in oncological
patients have been proposed, ranging from anatomical to functional assessments.
Changes in tumour size are not necessarily correlated with changes in tumour
viability and outcome. In addition, morphological changes resulting from therapy
occur slower than functional changes. Inclusion of PET images in radiotherapy
protocols is desirable because it is predictive of treatment response and provides
crucial information to accurately target the oncological lesion and to escalate the
radiation dose without increasing normal tissue injury. For this reason, PET may be
used for improving the Planning Treatment Volume (PTV). Nevertheless, due to the
nature of PET images (low spatial resolution, high noise and weak boundary),
metabolic image processing is a critical task.
The aim of this Ph.D thesis is to develope smart methodologies applied to the
medical imaging field to analyse different kind of problematic related to medical
images and data analysis, working closely to radiologist physicians.
Various issues in clinical environment have been addressed and a certain amount
of improvements has been produced in various fields, such as organs and tissues
segmentation and classification to delineate tumors volume using meshing learning
techniques to support medical decision.
In particular, the following topics have been object of this study:
• Technique for Crohn’s Disease Classification using Kernel Support Vector
Machine Based;
• Automatic Multi-Seed Detection For MR Breast Image Segmentation;
• Tissue Classification in PET Oncological Studies;
• KSVM-Based System for the Definition, Validation and Identification of the
Incisinal Hernia Reccurence Risk Factors;
• A smart and operator independent system to delineate tumours in Positron
Emission Tomography scans;
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• Active Contour Algorithm with Discriminant Analysis for Delineating
Tumors in Positron Emission Tomography;
• K-Nearest Neighbor driving Active Contours to Delineate Biological Tumor
Volumes;
• Tissue Classification to Support Local Active Delineation of Brain Tumors;
• A fully automatic system of Positron Emission Tomography Study
segmentation.
This work has been developed in collaboration with the medical staff and
colleagues at the:
• Dipartimento di Biopatologia e Biotecnologie Mediche e Forensi
(DIBIMED), University of Palermo
• Cannizzaro Hospital of Catania
• Istituto di Bioimmagini e Fisiologia Molecolare (IBFM) Centro Nazionale
delle Ricerche (CNR) of CefalĂą
• School of Electrical and Computer Engineering at Georgia Institute of
Technology
The proposed contributions have produced scientific publications in indexed
computer science and medical journals and conferences. They are very useful in
terms of PET and MRI image segmentation and may be used daily as a Medical
Decision Support Systems to enhance the current methodology performed by
healthcare operators in radiotherapy treatments.
The future developments of this research concern the integration of data acquired
by image analysis with the managing and processing of big data coming from a wide
kind of heterogeneous sources
Gravitational Clustering: A Simple, Robust and Adaptive Approach for Distributed Networks
Distributed signal processing for wireless sensor networks enables that
different devices cooperate to solve different signal processing tasks. A
crucial first step is to answer the question: who observes what? Recently,
several distributed algorithms have been proposed, which frame the
signal/object labelling problem in terms of cluster analysis after extracting
source-specific features, however, the number of clusters is assumed to be
known. We propose a new method called Gravitational Clustering (GC) to
adaptively estimate the time-varying number of clusters based on a set of
feature vectors. The key idea is to exploit the physical principle of
gravitational force between mass units: streaming-in feature vectors are
considered as mass units of fixed position in the feature space, around which
mobile mass units are injected at each time instant. The cluster enumeration
exploits the fact that the highest attraction on the mobile mass units is
exerted by regions with a high density of feature vectors, i.e., gravitational
clusters. By sharing estimates among neighboring nodes via a
diffusion-adaptation scheme, cooperative and distributed cluster enumeration is
achieved. Numerical experiments concerning robustness against outliers,
convergence and computational complexity are conducted. The application in a
distributed cooperative multi-view camera network illustrates the applicability
to real-world problems.Comment: 12 pages, 9 figure
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