DI2: prior-free and multi-item discretization of biological data and its applications

Abstract

Background A considerable number of data mining approaches for biomedical data analysis, including state-of-the-art associative models, require a form of data discretization. Although diverse discretization approaches have been proposed, they generally work under a strict set of statistical assumptions which are arguably insufficient to handle the diversity and heterogeneity of clinical and molecular variables within a given dataset. In addition, although an increasing number of symbolic approaches in bioinformatics are able to assign multiple items to values occurring near discretization boundaries for superior robustness, there are no reference principles on how to perform multi-item discretizations. Results In this study, an unsupervised discretization method, DI2, for variables with arbitrarily skewed distributions is proposed. Statistical tests applied to assess differences in performance confirm that DI2 generally outperforms well-established discretizations methods with statistical significance. Within classification tasks, DI2 displays either competitive or superior levels of predictive accuracy, particularly delineate for classifiers able to accommodate border values. Conclusions This work proposes a new unsupervised method for data discretization, DI2, that takes into account the underlying data regularities, the presence of outlier values disrupting expected regularities, as well as the relevance of border values. DI2 is available at https://github.com/JupitersMight/DI2

DISA tool: Discriminative and informative subspace assessment with categorical and numerical outcomes

“…This will reduce the number of redundant patterns. Numeric input variables are categorised with DI2 discretizer [ 25 ], with | L | = 3, | L | = 5, and | L | = 7 categories.…”

“…To test the DISA assessment of the patterns’ discriminative power, we considered an additional set of approaches: 1) approach, where the numerical outcome variable is discretized by applying DI2 [ 25 ] with | L | = 7, and the outcome is then interpreted as a class. In this case, DISA selects for each pattern the best fitting class ordered by lift.…”

“…discretized by applying DI2 [25] with |L| = 7, and the outcome is then interpreted as a class. In this case, DISA selects for each pattern the best fitting class ordered by lift.…”

“…These values confirm our initial hypothesis, that the classic and Average approaches form intervals that might not fully explore the discriminative profile of each pattern. The MinMax approach is able to fully accommodate noise and outlier pattern- . In this analysis, we compare the discriminative assessment produced over the range of discriminated outcomes with DISA against classic alternatives produced by discretizing the numerical outcome using DI2 [25] and the standard MinMax and Average approach (see Methods section) for the four datasets. The reference DISA values per function are presented in the last two rows.…”

“…An example of the aforementioned is an association rule like “ sex = female → mean wage = $7.90 p / hr ”, where they guarantee the rule’s discriminative properties by using classical measures, lift and confidence, and further ensure the validity of the outcome of interest by applying a Z-test; 2) discretization-based methods [ 24 ], which categorise the outcome variable in order to apply classic discriminative criteria in the discovery task. Well-known discretization methods categorise data based on frequency, user-inputted ranges, or more complex approaches such as the ones proposed by Alexandre et al [ 25 ] where numerical variables are fitted and categorised according to a continuous distribution. While not the same as discretization, fuzzy-logic-based approaches can also be used in the presence of quantitative [ 26 – 28 ], and continuous variables [ 29 ], to extract informative patterns; and 3) optimization-based methods [ 30 ], which consider stochastic searches that follow the idea of natural selection and genetics (e.g., particle swarm optimization methods).…”