Automatic and semantic pre — Selection of features using ontology for data mining on data sets related to cancer

Abstract:The data preprocessing is an essential step in knowledge discovery projects. The experts affirm that preprocessing tasks take between 50% to 70% of the total time of the knowledge discovery process. In this sense, several authors consider the data cleaning as one of the most cumbersome and critical tasks. Failure to provide high data quality in the preprocessing stage will significantly reduce the accuracy of any data analytic project. In this paper, we propose a framework to address the data quality issues in classification tasks DQF4CT. Our approach is composed of: (i) a conceptual framework to provide the user guidance on how to deal with data problems in classification tasks; and (ii) an ontology that represents the knowledge in data cleaning and suggests the proper data cleaning approaches. We presented two case studies through real datasets: physical activity monitoring (PAM) and occupancy detection of an office room (OD). With the aim of evaluating our proposal, the cleaned datasets by DQF4CT were used to train the same algorithms used in classification tasks by the authors of PAM and OD. Additionally, we evaluated DQF4CT through datasets of the Repository of Machine Learning Databases of the University of California, Irvine (UCI). In addition, 84% of the results achieved by the models of the datasets cleaned by DQF4CT are better than the models of the datasets authors.

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“…We could use the ontology of cancer diagnosis developed by [47] to select a subset of relevant features in datasets related to cancer.…”

Section: Discussionmentioning

confidence: 99%

“…From other domains, the data cleaning ontologies are also used for the construction of reservoir models [48], selection of features in datasets related to cancer [47], and preparation of genotype-phenotype relationships in a familial hypercholesterolemia dataset [49].…”

Section: Data Cleaning Ontologiesmentioning

confidence: 99%

From Theory to Practice: A Data Quality Framework for Classification Tasks

2018

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“…Including ontologies of specific domains to support some data quality issues; e.g, selection of relevant attributes based on expert knowledge. In the cancer domain, the ontology developed by [98] can be used for selecting the relevant attributes and avoid the use of algorithms with high computational complexity in dimensionality reduction tasks.…”

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confidence: 99%

How to Address the Data Quality Issues in Regression Models: A Guided Process for Data Cleaning

Corrales

Ledezma

2018

Symmetry

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Today, data availability has gone from scarce to superabundant. Technologies like IoT, trends in social media and the capabilities of smart-phones are producing and digitizing lots of data that was previously unavailable. This massive increase of data creates opportunities to gain new business models, but also demands new techniques and methods of data quality in knowledge discovery, especially when the data comes from different sources (e.g., sensors, social networks, cameras, etc.). The data quality process of the data set proposes conclusions about the information they contain. This is increasingly done with the aid of data cleaning approaches. Therefore, guaranteeing a high data quality is considered as the primary goal of the data scientist. In this paper, we propose a process for data cleaning in regression models (DC-RM). The proposed data cleaning process is evaluated through a real datasets coming from the UCI Repository of Machine Learning Databases. With the aim of assessing the data cleaning process, the dataset that is cleaned by DC-RM was used to train the same regression models proposed by the authors of UCI datasets. The results achieved by the trained models with the dataset produced by DC-RM are better than or equal to that presented by the datasets' authors.

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Automatic and semantic pre — Selection of features using ontology for data mining on data sets related to cancer

Cited by 2 publications

References 21 publications

From Theory to Practice: A Data Quality Framework for Classification Tasks

From Theory to Practice: A Data Quality Framework for Classification Tasks

How to Address the Data Quality Issues in Regression Models: A Guided Process for Data Cleaning

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