A MULTIPLE CLASSIFIER SYSTEM USING AMBIGUITY REJECTION FOR CLUSTERING-CLASSIFICATION COOPERATION

2000 ◽  
Vol 08 (06) ◽  
pp. 747-762 ◽  
Author(s):  
VEYIS GUNES ◽  
MICHEL MENARD ◽  
PIERRE LOONIS
Keyword(s):  
Fuzzy Clustering ◽  
Feature Space ◽  
Original Data ◽  
Point Of View ◽  
Training Data ◽  
Data Set ◽  
Classifier Selection ◽  
Fuzzy Clustering Method ◽  
Data Clusters ◽  
Multiple Classifier

This article aims at showing that supervised and unsupervised learnings are not competitive, but complementary methods. We propose to use a fuzzy clustering method with ambiguity rejection to guide the supervised learning performed by bayesian classifiers. This method detects ambiguous or mixed areas of a learning set. The problem is seen from the multi-decision point of view (i.e. several classification modules). Each classification module is specialized on a particular region of the feature space. These regions are obtained by fuzzy clustering and constitute the original data set by union. A classifier is associated with each cluster. The training set for each classifier is then defined on the cluster and its associated ambiguous clusters. The overall system is parallel, since different classifiers work with their own training data clusters. The algorithm makes possible the adaptive classifier selection in the sense that the fuzzy clustering with ambiguity rejection gives adapted training data regions of the feature space. The decision making is the fusion of outputs from the most adapted classifiers.

Parameter Prediction Based on Type-2 Fuzzy Clustering

2018 ◽  
Vol 26 (06) ◽  
pp. 877-892 ◽  
Author(s):  
Türkan Erbay Dalkiliç ◽  
Seda Sağirkaya
Keyword(s):  
Fuzzy Logic ◽  
Regression Analysis ◽  
Fuzzy Clustering ◽  
Alternative Methods ◽  
Data Set ◽  
Clustering Problem ◽  
Fuzzy Clustering Method ◽  
Parameter Prediction

In regression analysis, the data have different distributions which requires to go beyond the classical analysis during the prediction process. In such cases, the analysis method based on fuzzy logic is preferred as alternative methods. There are couple important steps in the regression analysis based on fuzzy logic. One of them is identification of the clusters that generate the data set, the other is the degree of memberships that are determined the grades of the contributions of the data contained in these clusters. In this study, parameter prediction based on type-2 fuzzy clustering is discussed. Firstly, type-1 fuzzy clustering problem was solved by the fuzzy c-means (FCM) method when the fuzzifier index is equal to two. Then the fuzzifier index m is defined as interval number. The membership degrees to the sets are determined by type-2 fuzzy clustering method. Membership degree obtained as a result of clustering based on type-1 and type-2 fuzzy logic are used as weight and parameter prediction using these membership degrees that determined by the proposed algorithm. Finally, the prediction result of the type-1 and type-2 fuzzy clustering parameter is compared with the error criterion based on the difference between observed values and the predicted values.

scholarly journals Embedding Undersampling Rotation Forest for Imbalanced Problem

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Huaping Guo ◽  
Xiaoyu Diao ◽  
Hongbing Liu
Keyword(s):  
Imbalanced Data ◽  
Feature Space ◽  
Original Data ◽  
Training Set ◽  
Data Set ◽  
Minority Class ◽  
Rotation Forest ◽  
Novel Method ◽  
Individual Classifier ◽  
The Cost

Rotation Forest is an ensemble learning approach achieving better performance comparing to Bagging and Boosting through building accurate and diverse classifiers using rotated feature space. However, like other conventional classifiers, Rotation Forest does not work well on the imbalanced data which are characterized as having much less examples of one class (minority class) than the other (majority class), and the cost of misclassifying minority class examples is often much more expensive than the contrary cases. This paper proposes a novel method called Embedding Undersampling Rotation Forest (EURF) to handle this problem (1) sampling subsets from the majority class and learning a projection matrix from each subset and (2) obtaining training sets by projecting re-undersampling subsets of the original data set to new spaces defined by the matrices and constructing an individual classifier from each training set. For the first method, undersampling is to force the rotation matrix to better capture the features of the minority class without harming the diversity between individual classifiers. With respect to the second method, the undersampling technique aims to improve the performance of individual classifiers on the minority class. The experimental results show that EURF achieves significantly better performance comparing to other state-of-the-art methods.

scholarly journals Why Do States Intervene in the Elections of Others? The Role of Incumbent–Opposition Divisions

2020 ◽  
pp. 1-22 ◽  
Author(s):  
Johannes Bubeck ◽  
Kai Jäger ◽  
Nikolay Marinov ◽  
Federico Nanni
Keyword(s):  
United States ◽  
Original Data ◽  
The United States ◽  
Polarization Measurement ◽  
Point Of View ◽  
Democratic Process ◽  
Data Set ◽  
Policy Positions ◽  
Us Government ◽  
The Government

Abstract Why do states intervene in elections abroad? This article argues that outsiders intervene when the main domestic contenders for office adopt policy positions that differ from the point of view of the outside power. It refers to the split between the government's and opposition's positions as policy polarization. Polarization between domestic political forces, rather than the degree of unfriendliness of the government in office, attracts two types of interventions: process (for or against democracy) and candidate (for or against the government) interventions. The study uses a novel, original data set to track local contenders’ policy positions. It shows that the new policy polarization measurement outperforms a number of available alternatives when it comes to explaining process and candidate interventions. The authors use this measurement to explain the behavior of the United States as an intervener in elections from 1945 to 2012. The United States is more likely to support the opposition, and the democratic process abroad, if a pro-US opposition is facing an anti-US government. It is more likely to support the government, and undermine the democratic process abroad, if a pro-US government is facing an anti-US opposition. The article also presents the results for all interveners, confirming the results from the US case.

Soft Sensor Modeling Based on Radial Basis Function Neural Network and Fuzzy C-Means

2011 ◽  
Vol 219-220 ◽  
pp. 1263-1266
Author(s):  
Xi Huai Wang ◽  
Jian Mei Xiao
Keyword(s):  
Neural Network ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Soft Sensor ◽  
Fuzzy Cluster ◽  
Training Data ◽  
Temperature Estimation ◽  
Data Set ◽  
Slab Temperature ◽  
Walking Beam Reheating Furnace

A neural network soft sensor based on fuzzy clustering is presented. The training data set is separated into several clusters with different centers, the number of fuzzy cluster is decided automatically, and the clustering centers are modified using an adaptive fuzzy clustering algorithm in the online stage. The proposed approach has been applied to the slab temperature estimation in a practical walking beam reheating furnace. Simulation results show that the approach is effective.

scholarly journals Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study

10.2196/26598 ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. e26598
Author(s):  
Dongchul Cha ◽  
MinDong Sung ◽  
Yu-Rang Park
Keyword(s):  
Domain Knowledge ◽  
Characteristic Curve ◽  
Feature Space ◽  
Original Data ◽  
Training Data ◽  
Data Governance ◽  
Domain Specific Knowledge ◽  
Partitioned Data ◽  
Vertically Partitioned Data ◽  
Latent Representations

Background Machine learning (ML) is now widely deployed in our everyday lives. Building robust ML models requires a massive amount of data for training. Traditional ML algorithms require training data centralization, which raises privacy and data governance issues. Federated learning (FL) is an approach to overcome this issue. We focused on applying FL on vertically partitioned data, in which an individual’s record is scattered among different sites. Objective The aim of this study was to perform FL on vertically partitioned data to achieve performance comparable to that of centralized models without exposing the raw data. Methods We used three different datasets (Adult income, Schwannoma, and eICU datasets) and vertically divided each dataset into different pieces. Following the vertical division of data, overcomplete autoencoder-based model training was performed for each site. Following training, each site’s data were transformed into latent data, which were aggregated for training. A tabular neural network model with categorical embedding was used for training. A centrally based model was used as a baseline model, which was compared to that of FL in terms of accuracy and area under the receiver operating characteristic curve (AUROC). Results The autoencoder-based network successfully transformed the original data into latent representations with no domain knowledge applied. These altered data were different from the original data in terms of the feature space and data distributions, indicating appropriate data security. The loss of performance was minimal when using an overcomplete autoencoder; accuracy loss was 1.2%, 8.89%, and 1.23%, and AUROC loss was 1.1%, 0%, and 1.12% in the Adult income, Schwannoma, and eICU dataset, respectively. Conclusions We proposed an autoencoder-based ML model for vertically incomplete data. Since our model is based on unsupervised learning, no domain-specific knowledge is required in individual sites. Under the circumstances where direct data sharing is not available, our approach may be a practical solution enabling both data protection and building a robust model.

scholarly journals Deep residual detection of radio frequency interference for FAST

2020 ◽  
Vol 492 (1) ◽  
pp. 1421-1431 ◽  
Author(s):  
Zhicheng Yang ◽  
Ce Yu ◽  
Jian Xiao ◽  
Bo Zhang
Keyword(s):  
Radio Frequency ◽  
Large Data ◽  
High Sensitivity ◽  
Original Data ◽  
Training Data ◽  
Radio Frequency Interference ◽  
Data Sets ◽  
Data Set ◽  
Time Required ◽  
Key Steps

ABSTRACT Radio frequency interference (RFI) detection and excision are key steps in the data-processing pipeline of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Because of its high sensitivity and large data rate, FAST requires more accurate and efficient RFI flagging methods than its counterparts. In the last decades, approaches based upon artificial intelligence (AI), such as codes using convolutional neural networks (CNNs), have been proposed to identify RFI more reliably and efficiently. However, RFI flagging of FAST data with such methods has often proved to be erroneous, with further manual inspections required. In addition, network construction as well as preparation of training data sets for effective RFI flagging has imposed significant additional workloads. Therefore, rapid deployment and adjustment of AI approaches for different observations is impractical to implement with existing algorithms. To overcome such problems, we propose a model called RFI-Net. With the input of raw data without any processing, RFI-Net can detect RFI automatically, producing corresponding masks without any alteration of the original data. Experiments with RFI-Net using simulated astronomical data show that our model has outperformed existing methods in terms of both precision and recall. Besides, compared with other models, our method can obtain the same relative accuracy with fewer training data, thus reducing the effort and time required to prepare the training data set. Further, the training process of RFI-Net can be accelerated, with overfittings being minimized, compared with other CNN codes. The performance of RFI-Net has also been evaluated with observing data obtained by FAST and the Bleien Observatory. Our results demonstrate the ability of RFI-Net to accurately identify RFI with fine-grained, high-precision masks that required no further modification.

scholarly journals Estimating the Support of a High-Dimensional Distribution

2001 ◽  
Vol 13 (7) ◽  
pp. 1443-1471 ◽  
Author(s):  
Bernhard Schölkopf ◽  
John C. Platt ◽  
John Shawe-Taylor ◽  
Alex J. Smola ◽  
Robert C. Williamson
Keyword(s):  
A Priori ◽  
Natural Extension ◽  
Feature Space ◽  
Weight Vector ◽  
Test Point ◽  
Training Data ◽  
Sequential Optimization ◽  
Support Vector ◽  
Small Subset ◽  
Data Set

Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a “simple” subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified value between 0 and 1. We propose a method to approach this problem by trying to estimate a function f that is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data.

scholarly journals Radiomics-based prediction of hemorrhage expansion among patients with thrombolysis/thrombectomy related-hemorrhagic transformation using machine learning

2021 ◽  
Vol 14 ◽  
pp. 175628642110600
Author(s):  
Junfeng Liu ◽  
Wendan Tao ◽  
Zhetao Wang ◽  
Xinyue Chen ◽  
Bo Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Calibration Curve ◽  
Original Data ◽  
Hemorrhagic Transformation ◽  
Training Data ◽  
Lasso Regression ◽  
Clinical Value ◽  
Brain Images ◽  
Data Set

Introduction: Patients with hemorrhagic transformation (HT) were reported to have hemorrhage expansion. However, identification these patients with high risk of hemorrhage expansion has not been well studied. Objectives: We aimed to develop a radiomic score to predict hemorrhage expansion after HT among patients treated with thrombolysis/thrombectomy during acute phase of ischemic stroke. Methods: A total of 104 patients with HT after reperfusion treatment from the West China hospital, Sichuan University, were retrospectively included in this study between 1 January 2012 and 31 December 2020. The preprocessed initial non-contrast-enhanced computed tomography (NECT) imaging brain images were used for radiomic feature extraction. A synthetic minority oversampling technique (SMOTE) was applied to the original data set. The after-SMOTE data set was randomly split into training and testing cohorts with an 8:2 ratio by a stratified random sampling method. The least absolute shrinkage and selection operator (LASSO) regression were applied to identify candidate radiomic features and construct the radiomic score. The performance of the score was evaluated by receiver operating characteristic (ROC) analysis and a calibration curve. Decision curve analysis (DCA) was performed to evaluate the clinical value of the model. Results: Among the 104 patients, 17 patients were identified with hemorrhage expansion after HT detection. A total of 154 candidate predictors were extracted from NECT images and five optimal features were ultimately included in the development of the radiomic score by using logistic regression machine-learning approach. The radiomic score showed good performance with high area under the curves in both the training data set (0.91, sensitivity: 0.83; specificity: 0.89), test data set (0.87, sensitivity: 0.60; specificity: 0.85), and original data set (0.82, sensitivity: 0.77; specificity: 0.78). The calibration curve and DCA also indicated that there was a high accuracy and clinical usefulness of the radiomic score for hemorrhage expansion prediction after HT. Conclusions: The currently established NECT-based radiomic score is valuable in predicting hemorrhage expansion after HT among patients treated with reperfusion treatment after ischemic stroke, which may aid clinicians in determining patients with HT who are most likely to benefit from anti-expansion treatment.

scholarly journals Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study (Preprint)

2020 ◽  
Author(s):  
Dongchul Cha ◽  
MinDong Sung ◽  
Yu-Rang Park
Keyword(s):  
Domain Knowledge ◽  
Characteristic Curve ◽  
Feature Space ◽  
Original Data ◽  
Training Data ◽  
Data Governance ◽  
Domain Specific Knowledge ◽  
Partitioned Data ◽  
Vertically Partitioned Data ◽  
Latent Representations

BACKGROUND Machine learning (ML) is now widely deployed in our everyday lives. Building robust ML models requires a massive amount of data for training. Traditional ML algorithms require training data centralization, which raises privacy and data governance issues. Federated learning (FL) is an approach to overcome this issue. We focused on applying FL on vertically partitioned data, in which an individual’s record is scattered among different sites. OBJECTIVE The aim of this study was to perform FL on vertically partitioned data to achieve performance comparable to that of centralized models without exposing the raw data. METHODS We used three different datasets (Adult income, Schwannoma, and eICU datasets) and vertically divided each dataset into different pieces. Following the vertical division of data, overcomplete autoencoder-based model training was performed for each site. Following training, each site’s data were transformed into latent data, which were aggregated for training. A tabular neural network model with categorical embedding was used for training. A centrally based model was used as a baseline model, which was compared to that of FL in terms of accuracy and area under the receiver operating characteristic curve (AUROC). RESULTS The autoencoder-based network successfully transformed the original data into latent representations with no domain knowledge applied. These altered data were different from the original data in terms of the feature space and data distributions, indicating appropriate data security. The loss of performance was minimal when using an overcomplete autoencoder; accuracy loss was 1.2%, 8.89%, and 1.23%, and AUROC loss was 1.1%, 0%, and 1.12% in the Adult income, Schwannoma, and eICU dataset, respectively. CONCLUSIONS We proposed an autoencoder-based ML model for vertically incomplete data. Since our model is based on unsupervised learning, no domain-specific knowledge is required in individual sites. Under the circumstances where direct data sharing is not available, our approach may be a practical solution enabling both data protection and building a robust model.

Intent Identification in Unattended Customer Queries Using an Unsupervised Approach

2021 ◽  
pp. 2150037
Author(s):  
Hugo D. Rebelo ◽  
Lucas A. F. de Oliveira ◽  
Gustavo M. Almeida ◽  
César A. M. Sotomayor ◽  
Geraldo L. Rochocz ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Language Processing ◽  
Communication Systems ◽  
Latent Dirichlet Allocation ◽  
Electric Energy ◽  
Original Data ◽  
Training Data ◽  
Data Set ◽  
Energy Company ◽  
Clustering Model

Customer’s satisfaction is crucial for companies worldwide. An integrated strategy composes omnichannel communication systems, in which chabot is widely used. This system is supervised, and the key point is that the required training data are originally unlabelled. Labelling data manually is unfeasible mainly nowadays due to the considerable volume. Moreover, customer behaviour is often hidden in the data even for experts. This work proposes a methodology to find unknown entities and intents automatically using unsupervised learning. This is based on natural language processing (NLP) for text data preparation and on machine learning (ML) for clustering model identification. Several combinations for preprocessing, vectorisation, dimensionality reduction and clustering techniques, were investigated. The case study refers to a Brazilian electric energy company, with a data set of failed customer queries, that is, not met by the company for any reason. They correspond to about 30% (4,044 queries) of the original data set. The best identified intent model employed stemming for preprocessing, word frequency analysis for vectorisation, latent Dirichlet allocation (LDA) for dimensionality reduction, and mini-batch [Formula: see text]-means for clustering. This system was able to allocate 62% of the failed queries in one of the seven found intents. For instance, this new labelled data can be used for the training of NLP-based chatbots contributing to a greater generalisation capacity, and ultimately, to increase customer satisfaction.

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