Content analysis based on text mining using genetic algorithm

Mukherjee, Indrajit; Al-Fayoumi, Mohammad; Mahanti, Prabhat Kumar; Jha, Ritesh; Albidewi, Ibrahim

doi:10.1109/icctd.2010.5645835

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Ozyurt (2012) used a GA-based feature selection technique to classify biomedical literature. Mukherjee et al (2010) used a GA to select optimal features for classifying e-mails. They form chromosomes from topics in the document and select the "parents" for the next generation by randomly choosing half of the chromosomes with the highest fitness.…”

Section: Feature Set Selectionmentioning

confidence: 99%

Using evolutionary algorithms to select text features for mining design rationale

Lester

Guerrero

Burge

2020

AIEDAM

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AbstractAt its heart, design is a decision-making process. These decisions, and the reasons for making them, comprise the design rationale (DR) for the designed artifact. If available, DR provides a comprehensive record of the reasoning behind the decisions made during the design. Unfortunately, while this information is potentially quite valuable, it is usually not explicitly captured. Instead, it is often buried in other design and development artifacts. In this paper, we study how to identify rationale from text documents, specifically software bug reports and design discussion transcripts. The method we examined is statistical text mining where a model is built to use document features to classify sentences. Choosing which features are most likely to be good predictors is important. We studied two evolutionary algorithms to optimize feature selection – ant colony optimization and genetic algorithms. We found that for many types of rationale, models built with an optimized feature set outperformed those built using all the document features.

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Section: Feature Set Selectionmentioning

confidence: 99%

Using evolutionary algorithms to select text features for mining design rationale

Lester

Guerrero

Burge

2020

AIEDAM

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“…Another area of great opportunity is in methodological hot topics such as the graph-theoretical and topological text mining methods which are very promising approaches, yet not much studied [122]. Much potential for further research has the application of evolutionary algorithms [123], for text mining [124].…”

Section: Conclusion and Future Outlookmentioning

confidence: 99%

Biomedical Text Mining: State-of-the-Art, Open Problems and Future Challenges

Holzinger

Schantl

Schroettner

et al. 2014

Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

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Text is a very important type of data within the biomedical domain. For example, patient records contain large amounts of text which has been entered in a non-standardized format, consequently posing a lot of challenges to processing of such data. For the clinical doctor the written text in the medical findings is still the basis for decision makingneither images nor multimedia data. However, the steadily increasing volumes of unstructured information need machine learning approaches for data mining, i.e. text mining. This paper provides a short, concise overview of some selected text mining methods, focusing on statistical methods, i.e. Latent Semantic Analysis, Probabilistic Latent Semantic Analysis, Latent Dirichlet Allocation, Hierarchical Latent Dirichlet Allocation, Principal Component Analysis, and Support Vector Machines, along with some examples from the biomedical domain. Finally, we provide some open problems and future challenges, particularly from the clinical domain, that we expect to stimulate future research.

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“…We pick a small subset of parent chromosomes with the highest fitness (average Naïve Bayes F-measure over 10 fold cross validation) to "reproduce" in every next generation [46]. This is an example of truncated selection [30], where only the top chromosomes can be selected for reproduction.…”

Section: Selectionmentioning

confidence: 99%

Mining Design Rationale from Software Documentation Using Nature Inspired Metaheuristics

Lester¹

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Design rationale refers to the explicit documentation of the reasons design decisions are made, the alternative design options considered, and the justification supporting these decisions. If properly recorded during the software development life cycle, design rationale documentation can be extremely useful to software engineers.However, because of the costs associated with maintaining extra documentation, it is often omitted. Fortunately, design rationale is naturally embedded in other documents that are created in the general course of software development -such as bug reports, project specifications, emails, or meeting transcripts. I will introduce the text mining problem of automatic extraction of design rationale from such software documentation, and will describe the machine learning based approach to capturing the rationale. I will discuss the feature selection step of the machine learning method, and give background on the genetic algorithm and ant colony optimization metaheuristic chosen in this research for the feature selection procedure. I will show that by applying these text mining techniques to classification of design rationale in software documentation, we are able to achieve higher F-measure than our estimate of what would be achievable manually.

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Content analysis based on text mining using genetic algorithm

Cited by 6 publications

References 11 publications

Using evolutionary algorithms to select text features for mining design rationale

Using evolutionary algorithms to select text features for mining design rationale

Biomedical Text Mining: State-of-the-Art, Open Problems and Future Challenges

Mining Design Rationale from Software Documentation Using Nature Inspired Metaheuristics

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