Automated Generation and Selection of Interpretable Features for Enterprise Security

Duan, Jiayi; Zeng, Ziheng; Oprea, Alina; Vasudevan, Shobha

doi:10.1109/bigdata.2018.8621986

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…It greedily adds constructed features to the training set, and therefore requires many computationally expensive evaluations. Duan et al (2018) leverage Fourier analysis of Boolean functions. From the input attributes, they first generate features in the disjunctive normal form (DNF) using the RIPPER decision rule learner (Cohen, 1995) and then extract the resulting Boolean features.…”

Section: Standalone Feature Constructionmentioning

confidence: 99%

Feature construction using explanations of individual predictions

Vouk,

Guid,

Robnik-Šikonja

2023

Preprint

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Feature construction can contribute to comprehensibility and performance of machine learning models. Unfortunately, it usually requires exhaustive search in the attribute space or time-consuming human involvement to generate meaningful features. We propose a novel heuristic approach for reducing the search space based on aggregation of instance-based explanations of predictive models. The proposed Explainable Feature Construction (EFC) methodology identifies groups of cooccurring attributes exposed by popular explanation methods, such as IME and SHAP. We empirically show that reducing the search to these groups significantly reduces the time of feature construction using logical, relational, Cartesian, numerical, and threshold (num-of-N and X-of-N) constructive operators. An analysis on 10 transparent synthetic datasets shows that EFC effectively identifies informative groups of attributes and constructs relevant features. Using 30 real-world classification datasets, we show significant improvements in classification accuracy for several classifiers and demonstrate the feasibility of the proposed feature construction even for large datasets. Finally, EFC generated interpretable features on a real-world problem from the financial industry, which were confirmed by a domain expert.

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Section: Standalone Feature Constructionmentioning

confidence: 99%

Feature construction using explanations of individual predictions

Vouk,

Guid,

Robnik-Šikonja

2023

Preprint

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“…The problem of manual hyperparameter tuning [13] inspired researchers to automate various blocks of the machine learning pipeline: feature engineering [14], meta-learning [15], architecture search [16] as well as full Combined Model Selection and Hyperparameter optimization [17] are the research lines which grabbed a great deal of attention in the past years. We review them in this order.…”

Section: State Of the Art In Automated MLmentioning

confidence: 99%

Automated Machine Learning in Practice: State of the Art and Recent Results

Tuggener

Amirian²,

Rombach³

et al. 2019

2019 6th Swiss Conference on Data Science (SDS)

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A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions. Building such models from data often involves the application of some form of machine learning. Thus, there is an ever growing demand in work force with the necessary skill set to do so. This demand has given rise to a new research topic concerned with fitting machine learning models fully automatically-AutoML. This paper gives an overview of the state of the art in AutoML with a focus on practical applicability in a business context, and provides recent benchmark results on the most important AutoML algorithms.

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