On Approaches to Discretization of Datasets Used for Evaluation of Decision Systems

Baron, Grzegorz; Harężlak, Katarzyna

doi:10.1007/978-3-319-39627-9_14

Cited by 13 publications

(8 citation statements)

References 8 publications

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“…That can lead to some inaccuracy during the evaluation of decision system. In [3] three approaches to discretization of test datasets were proposed: -"independent" (Id ) -training and test datasets are discretized separately, -"glued" (Gd ) -training and test datasets are concatenated, the obtained set is discretized, and finally resulting dataset is split back into learning and test sets, -"test on learn" (TLd ) -firstly training dataset is discretized, and then test set is processed using cut-points calculated for training data.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Another issue, which arose during the author's former research, was utilization of test sets in conjunction with discretization of input data [3]. There are fundamental questions, how discretize test datasets in relation to learning sets to keep both sets coherent.…”

Section: Introductionmentioning

confidence: 99%

“…Finally the following suite of classifiers was applied: Naive Bayes, decision tree C4.5, k -Nearest Neighbors k -NN, neural networks -multilayer perceptron and Radial Basis Function network -RBF, PART, Random Forest. Test were performed for non-discretized and discretized data applying different approaches to test datasets discretization [3].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Cross-Validation and Test Sets Approaches to Evaluation of Classifiers in Authorship Attribution Domain

Baron

2016

Communications in Computer and Information Science

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Abstract. The presented paper addresses problem of evaluation of decision systems in authorship attribution domain. Two typical approaches are cross-validation and evaluation based on specially created test datasets. Sometimes preparation of test sets can be troublesome. Another problem appears when discretization of input sets is taken into account. It is not obvious how to discretize test datasets. Therefore model evaluation method not requiring test sets would be useful. Cross-validation is the well-known and broadly accepted method, so the question arose if it can deliver reliable information about quality of prepared decision system. The set of classifiers was selected and different discretization algorithms were applied to obtain method invariant outcomes. The comparative results of experiments performed using cross-validation and test sets approaches to system evaluation, and conclusions are presented.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Cross-Validation and Test Sets Approaches to Evaluation of Classifiers in Authorship Attribution Domain

Baron

2016

Communications in Computer and Information Science

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“…Applying definitions of categories obtained for all features, in the third step conditions in previously induced rules were discretised, translating decision algorithms from real into discrete space. And finally, the characteristics of discretised systems of rules, such as coverage and reduction of storage requirements, were studied, while performance was evaluated by application of rule classifiers to independently discretised test sets [10,11]. With this new approach several discretisation methods could be considered for a task at a lower cost.…”

Section: Introductionmentioning

confidence: 99%

Discretisation of conditions in decision rules induced for continuous data

2020

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Typically discretisation procedures are implemented as a part of initial pre-processing of data, before knowledge mining is employed. It means that conclusions and observations are based on reduced data, as usually by discretisation some information is discarded. The paper presents a different approach, with taking advantage of discretisation executed after data mining. In the described study firstly decision rules were induced from real-valued features. Secondly, data sets were discretised. Using categories found for attributes, in the third step conditions included in inferred rules were translated into discrete domain. The properties and performance of rule classifiers were tested in the domain of stylometric analysis of texts, where writing styles were defined through quantitative attributes of continuous nature. The performed experiments show that the proposed processing leads to sets of rules with significantly reduced sizes while maintaining quality of predictions, and allows to test many data discretisation methods at the acceptable computational costs.

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“…As attribute values specified usage frequencies of textual descriptors, they were small fractions, which means that for data mining there was needed either some technique that can deal efficiently with continuous numbers, or some discretization strategy was required [2]. Since regardless of a selected method discretization always causes some loss of information, it was not attempted.…”

Section: Input Datasetsmentioning

confidence: 99%

Weighting and Pruning of Decision Rules by Attributes and Attribute Rankings

Stańczyk

2016

Communications in Computer and Information Science

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Abstract. Pruning is a popular post-processing mechanism used in search for optimal solutions when there is insufficient domain knowledge to either limit learning data or govern induction in order to infer only the most interesting or important decision rules. Filtering of generated rules can be driven by various parameters, for example explicit rule characteristics. The paper presents research on pruning rule sets by two approaches involving attribute rankings, the first relaying on selection of rules referring to the highest ranking attributes, which is compared to weighting of rules by calculated quality measures dependent on weights coming from attribute rankings that results in rule ranking.

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On Approaches to Discretization of Datasets Used for Evaluation of Decision Systems

Cited by 13 publications

References 8 publications

Comparison of Cross-Validation and Test Sets Approaches to Evaluation of Classifiers in Authorship Attribution Domain

Comparison of Cross-Validation and Test Sets Approaches to Evaluation of Classifiers in Authorship Attribution Domain

Discretisation of conditions in decision rules induced for continuous data

Weighting and Pruning of Decision Rules by Attributes and Attribute Rankings

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