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Comprehensive decision tree models in bioinformatics.

Stiglic G, Kocbek S, Pernek I, Kokol P - PLoS ONE (2012)

Bottom Line: Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees.Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree.The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Health Sciences, University of Maribor, Maribor, Slovenia. gregor.stiglic@uni-mb.si

ABSTRACT

Purpose: Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible.

Methods: This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree.

Results: The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree.

Conclusions: The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics.

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Related in: MedlinePlus

Comparison of durations for different datasets.
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pone-0033812-g003: Comparison of durations for different datasets.

Mentions: The second example presents two decision trees built on the protein solubility dataset with all features (Figure 3). In this case the accuracy and AUC were both improved significantly, when the size of the decision tree model was reduced. This is possible due to binary class attribute that still allows effective trees that are much smaller than the original pruned J48 trees.


Comprehensive decision tree models in bioinformatics.

Stiglic G, Kocbek S, Pernek I, Kokol P - PLoS ONE (2012)

Comparison of durations for different datasets.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3316502&req=5

pone-0033812-g003: Comparison of durations for different datasets.
Mentions: The second example presents two decision trees built on the protein solubility dataset with all features (Figure 3). In this case the accuracy and AUC were both improved significantly, when the size of the decision tree model was reduced. This is possible due to binary class attribute that still allows effective trees that are much smaller than the original pruned J48 trees.

Bottom Line: Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees.Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree.The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Health Sciences, University of Maribor, Maribor, Slovenia. gregor.stiglic@uni-mb.si

ABSTRACT

Purpose: Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible.

Methods: This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree.

Results: The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree.

Conclusions: The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics.

Show MeSH
Related in: MedlinePlus