Lazy attribute selection: Choosing attributes at classification time

Pereira, Rafael B.; Plastino, Alexandre; Zadrozny, Bianca; Merschmann, Luiz Henrique de Campos; Freitas, Alex A.

doi:10.3233/ida-2011-0491

Cited by 13 publications

(12 citation statements)

References 15 publications

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“…Then, the built classifier is used for classifying all testing instances. In contrast, lazy learning methods build a classifier only in the testing phase, when a new testing instance to be classified is observed [1,14]. Hence, an individual classifier is built for each testing instance.…”

Section: Eager Learning and Lazy Learningmentioning

confidence: 99%

Two methods for constructing a gene ontology-based feature network for a Bayesian network classifier and applications to datasets of aging-related genes

Wan

Freitas

2015

Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics

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The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record. EnquiriesFor any further enquiries regarding the licence status of this document, please contact: researchsupport@kent.ac.uk If you believe this document infringes copyright then please contact the KAR admin team with the take-down information provided at http://kar.kent.ac.uk/contact.html Wan, Cen and Freitas, Alex A. (2015) Two methods for constructing a gene ontology-based feature network for a Bayesian network classifier and applications to datasets of aging-related genes. Citation for published version ABSTRACTIn the context of the classification task of data mining or machine learning, hierarchical feature selection methods exploit hierarchical relationships among features in order to select a subset of features without hierarchical redundancy. Hierarchical feature selection is a new research area in classification research, since nearly all feature selection methods ignore hierarchical relationships among features. This paper proposes two methods for constructing a network of features to be used by a Bayesian Network Augmented Naïve Bayes (BAN) classifier, in datasets of aging-related genes where Gene Ontology (GO) terms are used as hierarchically related predictive features. One of the BAN network construction method relies on a hierarchical feature selection method to detect and remove hierarchical redundancies among features (GO terms); whilst the other BAN network construction method simply uses a conventional, flat feature selection method to select features, without removing the hierarchical redundancies associated with the GO. Both BAN network construction methods may create new edges among nodes (features) in the BAN network that did not exist in the original GO DAG (Directed Acyclic Graph), in order to preserve the generalization-specialization (ancestor-descendant) relationship among selected features. Experiments comparing these two BAN network construction methods, when using two different hierarchical feature selection methods and one flat feature selection method, have shown that the best results are obtained by the BAN network construction method using one type of hierarchical feature selection method, i.e., select Hierarchical Information-Preserving features (HIP).

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Section: Eager Learning and Lazy Learningmentioning

confidence: 99%

Two methods for constructing a gene ontology-based feature network for a Bayesian network classifier and applications to datasets of aging-related genes

Wan

Freitas

2015

Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics

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“…DM involves the study of tasks and techniques, where tasks are a specific class of problems and techniques are the groups of solutions to solve them [6]. …”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…KDD is the process of finding knowledge in data. In this context, DM is the step of obtaining the information [6]. …”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Predictive tasks search for patterns to infer new information about the existing data or to predict the behaviour of new data. The main predictive tasks are classification and regression [6, 19]. …”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Data mining (DM) is the process of extracting or mining knowledge from a large volume of data and there are several algorithms that can be used to discover patterns in a dataset [6]. This work focuses on the use of decision trees for knowledge discovery in a dataset of brain signals of blind (visual impairment) and sighted people during an activity involving spatial abilities in order to discover whether there is a difference in their brain activity.…”

Section: Introductionmentioning

confidence: 99%

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Discovering Patterns in Brain Signals Using Decision Trees

Bastos

Adamatti

Billa

2016

Computational Intelligence and Neuroscience

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Even with emerging technologies, such as Brain-Computer Interfaces (BCI) systems, understanding how our brains work is a very difficult challenge. So we propose to use a data mining technique to help us in this task. As a case of study, we analyzed the brain's behaviour of blind people and sighted people in a spatial activity. There is a common belief that blind people compensate their lack of vision using the other senses. If an object is given to sighted people and we asked them to identify this object, probably the sense of vision will be the most determinant one. If the same experiment was repeated with blind people, they will have to use other senses to identify the object. In this work, we propose a methodology that uses decision trees (DT) to investigate the difference of how the brains of blind people and people with vision react against a spatial problem. We choose the DT algorithm because it can discover patterns in the brain signal, and its presentation is human interpretable. Our results show that using DT to analyze brain signals can help us to understand the brain's behaviour.

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Introduction

Wan¹

2018

Advanced Information and Knowledge Processing

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Lazy attribute selection: Choosing attributes at classification time

Cited by 13 publications

References 15 publications

Two methods for constructing a gene ontology-based feature network for a Bayesian network classifier and applications to datasets of aging-related genes

Two methods for constructing a gene ontology-based feature network for a Bayesian network classifier and applications to datasets of aging-related genes

Discovering Patterns in Brain Signals Using Decision Trees

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