@article {1414,
	title = {Automated Analysis of Regularities Between Model Parameters and Output Using Support Vector Regression in Conjunction with Decision Trees},
	journal = {Journal of Artificial Societies and Social Simulation},
	volume = {21},
	year = {2018},
	pages = {1},
	abstract = {<p>Opening the black-box of nonlinear relationships between model inputs and outputs, significantly contributes to the understanding of the dynamic problem being studied. Considering the weaknesses and disadvantages of human-guided and systematic techniques offered in the literature, this paper presents a model analysis and exploration tool for agent-based models. The tool first approximates input-output relationships by developing a metamodel, a simplified representation of the original agent-based model. For this purpose, it utilizes support vector regression, which is capable of approximating highly nonlinear systems accurately. Following metamodel fitting, the tool incorporates a tree-based method to extract knowledge embedded in the metamodel. The resulting tree is then expressed as a set of IF-THEN rules that have high comprehensibility compared to complex metamodel function. We utilized the tool for the exploration of Traffic Basic model and the results show the relationship between model input and output. Furthermore, rules extracted from the metamodel point out certain counter-intuitive results of the model which are not easily inferred from the raw input-output data. We also discuss potential uses of our tool and provide the R script which makes the analysis repeatable for other agent-based models.</p>},
	keywords = {decision tree, MetaModel, R, Rule Extraction, Support Vector Regression, Traffic},
	issn = {1460-7425},
	doi = {10.18564/jasss.3786},
	url = {http://jasss.soc.surrey.ac.uk/21/4/1.html},
	author = {Edali, Mert and G{\"o}nen{\c c} Y{\"u}cel}
}
@article {35,
	title = {SMT: Sparse multivariate tree},
	journal = {Statistical Analysis and Data Mining},
	volume = {7},
	year = {2014},
	month = {02/2014},
	pages = {53-69},
	abstract = {<p style="text-align: justify;">A multivariate decision tree attempts to improve upon the single variable split in a traditional tree. With the increase in datasets with many features and a small number of labeled instances in a variety of domains (bioinformatics, text mining, etc.), a traditional tree-based approach with a greedy variable selection at a node may omit important information. Therefore, the recursive partitioning idea of a simple decision tree combined with the intrinsic feature selection of L1 regularized logistic regression (LR) at each node is a natural choice for a multivariate tree model that is simple, but broadly applicable. This natural solution leads to the sparse multivariate tree (SMT) considered here. SMT can naturally handle non-time-series data and is extended to handle time-series classification problems with the power of extracting interpretable temporal patterns (e.g., means, slopes, and deviations). Binary L1 regularized LR models are used here for binary classification problems. However, SMT may be extended to solve multiclass problems with multinomial LR models. The accuracy and computational efficiency of SMT is compared to a large number of competitors on time series and non-time-series data.</p>},
	keywords = {decision tree, feature extraction, fused Lasso, Lasso, time series classification},
	issn = {1932-1872},
	doi = {10.1002/sam.11208},
	url = {http://dx.doi.org/10.1002/sam.11208},
	author = {Houtao Deng and Mustafa Gokce Baydogan and George Runger}
}
