scholarly journals Prototype Selection Using Clustering and Conformance Metrics for Process Discovery

2020 ◽  
pp. 281-294
Author(s):  
Mohammadreza Fani Sani ◽  
Mathilde Boltenhagen ◽  
Wil van der Aalst
Keyword(s):  
Prototype Selection ◽  
Process Discovery

BiLabel-Specific Features for Multi-Label Classification

2021 ◽  
Vol 16 (1) ◽  
pp. 1-23
Author(s):  
Min-Ling Zhang ◽  
Jun-Peng Fang ◽  
Yi-Bo Wang
Keyword(s):  
Predictive Models ◽  
Comparative Studies ◽  
State Of The Art ◽  
Classification Model ◽  
Generation Process ◽  
Prototype Selection ◽  
Class Label ◽  
Benchmark Datasets ◽  
Label Correlations ◽  
Class Labels

In multi-label classification, the task is to induce predictive models which can assign a set of relevant labels for the unseen instance. The strategy of label-specific features has been widely employed in learning from multi-label examples, where the classification model for predicting the relevancy of each class label is induced based on its tailored features rather than the original features. Existing approaches work by generating a group of tailored features for each class label independently, where label correlations are not fully considered in the label-specific features generation process. In this article, we extend existing strategy by proposing a simple yet effective approach based on BiLabel-specific features. Specifically, a group of tailored features is generated for a pair of class labels with heuristic prototype selection and embedding. Thereafter, predictions of classifiers induced by BiLabel-specific features are ensembled to determine the relevancy of each class label for unseen instance. To thoroughly evaluate the BiLabel-specific features strategy, extensive experiments are conducted over a total of 35 benchmark datasets. Comparative studies against state-of-the-art label-specific features techniques clearly validate the superiority of utilizing BiLabel-specific features to yield stronger generalization performance for multi-label classification.

scholarly journals Semantics of Voids within Data: Ignorance-Aware Machine Learning

2021 ◽  
Vol 10 (4) ◽  
pp. 246
Author(s):  
Vagan Terziyan ◽  
Anton Nikulin
Keyword(s):  
Spatial Data ◽  
Information Science ◽  
Nearest Neighbor ◽  
Geographical Information ◽  
Two Dimensional ◽  
Prototype Selection ◽  
Data Space ◽  
Important Concern ◽  
Traditional Classification ◽  
Nearest Neighbor Classifiers

Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, known, and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples that are not yet observed, known, or understood. These tools traditionally take semantically labeled samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach, and we suggest considering the things the other way around. What if the task would be as follows: how to build a model based on the semantics of our ignorance, i.e., by processing the shape of “voids” within the available data space? Can we improve traditional classification by also modeling the ignorance? In this paper, we provide some algorithms for the discovery and visualization of the ignorance zones in two-dimensional data spaces and design two ignorance-aware smart prototype selection techniques (incremental and adversarial) to improve the performance of the nearest neighbor classifiers. We present experiments with artificial and real datasets to test the concept of the usefulness of ignorance semantics discovery.

scholarly journals Improved Prototype Selection in Synergetic Pattern Recognition to Recognize Human Face Expressions

2013 ◽  
Vol 7 (4) ◽  
pp. 541-552
Author(s):  
Minchen Zhu ◽  
Weizhi Wang ◽  
Binghan Liu ◽  
Jingshan Huang
Keyword(s):  
Pattern Recognition ◽  
Prototype Selection ◽  
Human Face ◽  
Face Expressions

Decremental Sparse Modeling Representative Selection for prototype selection

2015 ◽  
Vol 48 (11) ◽  
pp. 3714-3727 ◽  
Author(s):  
F. Dornaika ◽  
I. Kamal Aldine
Keyword(s):  
Prototype Selection ◽  
Sparse Modeling ◽  
Representative Selection ◽  
Selection For

Using Life Cycle Information in Process Discovery

2016 ◽  
pp. 204-217 ◽  
Author(s):  
Sander J. J. Leemans ◽  
Dirk Fahland ◽  
Wil M. P. van der Aalst
Keyword(s):  
Life Cycle ◽  
Process Discovery
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