A novel deep fuzzy classifier by stacking adversarial interpretable TSK fuzzy sub-classifiers with smooth gradient information
S Gu, FL Chung, S Wang - IEEE Transactions on Fuzzy …, 2019 - ieeexplore.ieee.org
Different from our previous stacked-structure-based deep fuzzy classifier, in this paper, we
explore the distinctive role of adversarial outputs of training samples in enhancing the …
explore the distinctive role of adversarial outputs of training samples in enhancing the …
Particle swarm optimization for network-based data classification
Complex networks provide a powerful tool for data representation due to its ability to
describe the interplay between topological, functional, and dynamical properties of the input …
describe the interplay between topological, functional, and dynamical properties of the input …
Link prediction in complex networks based on cluster information
JC Valverde-Rebaza, A de Andrade Lopes - Advances in Artificial …, 2012 - Springer
Cluster in graphs is densely connected group of vertices sparsely connected to other
groups. Hence, for prediction of a future link between a pair of vertices, these vertices …
groups. Hence, for prediction of a future link between a pair of vertices, these vertices …
Organizational Data Classification Based on the Importance Concept of Complex Networks
MG Carneiro, L Zhao - IEEE transactions on neural networks …, 2017 - ieeexplore.ieee.org
Data classification is a common task, which can be performed by both computers and
human beings. However, a fundamental difference between them can be observed …
human beings. However, a fundamental difference between them can be observed …
A comparative study of the leading machine learning techniques and two new optimization algorithms
P Baumann, DS Hochbaum, YT Yang - European journal of operational …, 2019 - Elsevier
We present here a computational study comparing the performance of leading machine
learning techniques to that of recently developed graph-based combinatorial optimization …
learning techniques to that of recently developed graph-based combinatorial optimization …
A scheme for high level data classification using random walk and network measures
TH Cupertino, MG Carneiro, Q Zheng, J Zhang… - Expert Systems with …, 2018 - Elsevier
Supervised classification techniques are known to exploit physical information of the
analysed data, such as similarity, distribution and other low level features. Despite the …
analysed data, such as similarity, distribution and other low level features. Despite the …
Graph-guided higher-order attention network for industrial rotating machinery intelligent fault diagnosis
Data-driven approaches have gained great success in the field of rotating machinery fault
diagnosis for its powerful feature representation capability. However, in most of the current …
diagnosis for its powerful feature representation capability. However, in most of the current …
Classification using link prediction
Link prediction in a graph is the problem of detecting the missing links or the ones that would
be formed in the near future. Using a graph representation of the data, we can convert the …
be formed in the near future. Using a graph representation of the data, we can convert the …
Musical rhythmic pattern extraction using relevance of communities in networks
The rhythmic background of a musical piece is usually composed of featured elements that
define the musical genre. For each song, such elements form rhythmic patterns, the most …
define the musical genre. For each song, such elements form rhythmic patterns, the most …
An incremental learning algorithm based on the K-associated graph for non-stationary data classification
Non-stationary classification problems concern the changes on data distribution over a
classifier lifetime. To face this problem, learning algorithms must conciliate essential, but …
classifier lifetime. To face this problem, learning algorithms must conciliate essential, but …