Natural Language Explanation Model for Decision Trees

This study describes a model of explanations in natural language for classification decision trees. The explanations include global aspects of the classifier and local aspects of the classification of a particular instance. The proposal is implemented in the ExpliClas open source Web service [1], which in its current version operates on trees built with Weka and data sets with numerical attributes. The feasibility of the proposal is illustrated with two example cases, where the detailed explanation of the respective classification trees is shown.

[1]  Tobias Scheffer,et al.  Finding association rules that trade support optimally against confidence , 2001, Intell. Data Anal..

[2]  Anneleen Van Assche,et al.  Ensemble Methods for Noise Elimination in Classification Problems , 2003, Multiple Classifier Systems.

[3]  Amelec Viloria,et al.  Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data) , 2018, DMBD.

[4]  Yuxuan Wang,et al.  A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network , 2007, 2007 IEEE International Symposium on Signal Processing and Information Technology.

[5]  Emiel Krahmer,et al.  Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation , 2017, J. Artif. Intell. Res..

[6]  Francisco Herrera,et al.  Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce , 2018, Inf. Fusion.

[7]  Amelec Viloria,et al.  Measures of Concentration and Stability: Two Pedagogical Tools for Industrial Organization Courses , 2018, ICSI.

[8]  Solon Barocas,et al.  Engaging the ethics of data science in practice , 2017, Commun. ACM.

[9]  Patrick Wendell,et al.  Learning Spark: Lightning-Fast Big Data Analytics , 2015 .

[10]  Ravi Sankar,et al.  Time Series Prediction Using Support Vector Machines: A Survey , 2009, IEEE Computational Intelligence Magazine.

[11]  Francisco Herrera,et al.  Principal Components Analysis Random Discretization Ensemble for Big Data , 2018, Knowl. Based Syst..

[12]  José M. Alonso,et al.  An exploratory study on the benefits of using natural language for explaining fuzzy rule-based systems , 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[13]  Amelec Viloria,et al.  Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company , 2016 .

[14]  Jimmy J. Lin.  MapReduce is Good Enough? If All You Have is a Hammer, Throw Away Everything That's Not a Nail! , 2012, Big Data.

[15]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[16]  Georg Ruß,et al.  Data Mining of Agricultural Yield Data: A Comparison of Regression Models , 2009, ICDM.

[17]  Michael J. Franklin,et al.  Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing , 2012, NSDI.

[18]  Trevor Darrell,et al.  Generating Visual Explanations , 2016, ECCV.

[19]  Mugdha Jain,et al.  Adapting k-means for Clustering in Big Data , 2014 .