Decision Tree and Na&amp;#239;ve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Karim, Masud; Rahman, Rashedur M.

doi:10.4236/jsea.2013.64025

Cited by 102 publications

(46 citation statements)

References 6 publications

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“…Techniques have also been proposed to postprocess decision trees to extract actionable knowledge [31,19,30,12]. For example, Yang et al considers the problem of suggesting actions that maximize the expected profit based a decision tree model [31].…”

Section: Related Workmentioning

confidence: 99%

Optimal Action Extraction for Random Forests and Boosted Trees

Cui

Chen

et al. 2015

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Additive tree models (ATMs) are widely used for data mining and machine learning. Important examples of ATMs include random forest, adaboost (with decision trees as weak learners), and gradient boosted trees, and they are often referred to as the best off-the-shelf classifiers. Though capable of attaining high accuracy, ATMs are not well interpretable in the sense that they do not provide actionable knowledge for a given instance. This greatly limits the potential of ATMs on many applications such as medical prediction and business intelligence, where practitioners need suggestions on actions that can lead to desirable outcomes with minimum costs.To address this problem, we present a novel framework to post-process any ATM classifier to extract an optimal actionable plan that can change a given input to a desired class with a minimum cost. In particular, we prove the NPhardness of the optimal action extraction problem for ATMs and formulate this problem in an integer linear programming formulation which can be efficiently solved by existing packages. We also empirically demonstrate the effectiveness of the proposed framework by conducting comprehensive experiments on challenging real-world datasets.

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Section: Related Workmentioning

confidence: 99%

Optimal Action Extraction for Random Forests and Boosted Trees

Cui

Chen

et al. 2015

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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“…Complementary to interpretability, a number of studies have focused on actionable knowledge extraction [35,37], where the focus is placed on identifying a transparent series of input feature changes intended to transform particular model predictions to a desired output with low cost. Many actionability studies exist with a business and marketing orientation, investigating actions necessary to alter customer behavior for mostly tree-based models [19,41]. In addition, several studies place particular focus on actionability which can be performed in an efficient and optimal manner [12,36].…”

Section: Related Workmentioning

confidence: 99%

Locally and globally explainable time series tweaking

et al. 2019

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Time series classification has received great attention over the past decade with a wide range of methods focusing on predictive performance by exploiting various types of temporal features. Nonetheless, little emphasis has been placed on interpretability and explainability. In this paper, we formulate the novel problem of explainable time series tweaking, where, given a time series and an opaque classifier that provides a particular classification decision for the time series, we want to find the changes to be performed to the given time series so that the classifier changes its decision to another class. We show that the problem is NP-hard, and focus on three instantiations of the problem using global and local transformations. In the former case, we investigate the k-nearest neighbor classifier and provide an algorithmic solution to the global time series tweaking problem. In the latter case, we investigate the random shapelet forest classifier and focus on two instantiations of the local time series tweaking problem, which we refer to as reversible and irreversible time series tweaking, and propose two algorithmic solutions for the two problems along with simple optimizations. An extensive experimental evaluation on a variety of real datasets demonstrates the usefulness and effectiveness of our problem formulation and solutions.

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“…Complementary to interpretability, a number of studies have focused on actionable knowledge extraction, where the focus is placed on identifying a transparent series of input feature changes intended to transform particular model predictions to a desired output with low cost. Many actionability studies exist with a business and marketing orientation, investigating actions necessary to alter customer behaviour for mostly treebased models [30], [31]. In addition, several studies place particular focus on actionability which can be performed in an efficient and optimal manner [32], [33].…”

Section: Related Workmentioning

confidence: 99%

Explainable Time Series Tweaking via Irreversible and Reversible Temporal Transformations

Karlsson

Rebane

Papapetrou

et al. 2018

2018 IEEE International Conference on Data Mining (ICDM)

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Time series classification has received great attention over the past decade with a wide range of methods focusing on predictive performance by exploiting various types of temporal features. Nonetheless, little emphasis has been placed on interpretability and explainability. In this paper, we formulate the novel problem of explainable time series tweaking, where, given a time series and an opaque classifier that provides a particular classification decision for the time series, we want to find the minimum number of changes to be performed to the given time series so that the classifier changes its decision to another class. We show that the problem is NP-hard, and focus on two instantiations of the problem, which we refer to as reversible and irreversible time series tweaking. The classifier under investigation is the random shapelet forest classifier. Moreover, we propose two algorithmic solutions for the two problems along with simple optimizations, as well as a baseline solution using the nearest neighbor classifier. An extensive experimental evaluation on a variety of real datasets demonstrates the usefulness and effectiveness of our problem formulation and solutions.

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Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Cited by 102 publications

References 6 publications

Optimal Action Extraction for Random Forests and Boosted Trees

Optimal Action Extraction for Random Forests and Boosted Trees

Locally and globally explainable time series tweaking

Explainable Time Series Tweaking via Irreversible and Reversible Temporal Transformations

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