NiaAutoARM: Automated generation and evaluation of Association Rule Mining pipelines

• URL: http://arxiv.org/abs/2501.00138v1
• Date: Mon, 30 Dec 2024 20:48:51 GMT
• Title: NiaAutoARM: Automated generation and evaluation of Association Rule Mining pipelines
• Authors: Uroš Mlakar, Iztok Fister Jr., Iztok Fister,
• Abstract summary: We propose a novel Automated Machine Learning method, NiaAutoARM, for constructing the full association rule mining pipelines. Along with the theoretical representation of the proposed method, we also present a comprehensive experimental evaluation.
• Score: 0.8848340429852071
• License:
• Abstract: The Numerical Association Rule Mining paradigm that includes concurrent dealing with numerical and categorical attributes is beneficial for discovering associations from datasets consisting of both features. The process is not considered as easy since it incorporates several processing steps running sequentially that form an entire pipeline, e.g., preprocessing, algorithm selection, hyper-parameter optimization, and the definition of metrics evaluating the quality of the association rule. In this paper, we proposed a novel Automated Machine Learning method, NiaAutoARM, for constructing the full association rule mining pipelines based on stochastic population-based meta-heuristics automatically. Along with the theoretical representation of the proposed method, we also present a comprehensive experimental evaluation of the proposed method.

Related papers

• Faster WIND: Accelerating Iterative Best-of-$N$ Distillation for LLM Alignment [81.84950252537618]
  This paper reveals a unified game-theoretic connection between iterative BOND and self-play alignment. We establish a novel framework, WIN rate Dominance (WIND), with a series of efficient algorithms for regularized win rate dominance optimization.
  arXiv  Detail & Related papers  (2024-10-28T04:47:39Z)
• Efficient Learning of POMDPs with Known Observation Model in Average-Reward Setting [56.92178753201331]
  We propose the Observation-Aware Spectral (OAS) estimation technique, which enables the POMDP parameters to be learned from samples collected using a belief-based policy. We show the consistency of the OAS procedure, and we prove a regret guarantee of order $mathcalO(sqrtT log(T)$ for the proposed OAS-UCRL algorithm.
  arXiv  Detail & Related papers  (2024-10-02T08:46:34Z)
• Online Variational Sequential Monte Carlo [49.97673761305336]
  We build upon the variational sequential Monte Carlo (VSMC) method, which provides computationally efficient and accurate model parameter estimation and Bayesian latent-state inference. Online VSMC is capable of performing efficiently, entirely on-the-fly, both parameter estimation and particle proposal adaptation.
  arXiv  Detail & Related papers  (2023-12-19T21:45:38Z)
• Self Optimisation and Automatic Code Generation by Evolutionary Algorithms in PLC based Controlling Processes [0.0]
  A novel approach based on evolutionary algorithms is proposed to self optimise the system logic of complex processes. The presented approach is evaluated on an industrial liquid station process subject to a multi-objective problem.
  arXiv  Detail & Related papers  (2023-04-12T06:36:54Z)
• Naive Automated Machine Learning [0.0]
  We present Naive AutoML, an approach that does precisely this: It optimize the different algorithms of a pre-defined pipeline scheme in isolation. The isolated generalization leads to substantially reduced search spaces.
  arXiv  Detail & Related papers  (2021-11-29T13:12:54Z)
• Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines [48.7576911714538]
  The proposed approach is aimed to automate the design of composite machine learning pipelines. It designs the pipelines with a customizable graph-based structure, analyzes the obtained results, and reproduces them. The software implementation on this approach is presented as an open-source framework.
  arXiv  Detail & Related papers  (2021-06-26T23:19:06Z)
• Incremental Search Space Construction for Machine Learning Pipeline Synthesis [4.060731229044571]
  Automated machine learning (AutoML) aims for constructing machine learning (ML) pipelines automatically. We propose a data-centric approach based on meta-features for pipeline construction. We prove the effectiveness and competitiveness of our approach on 28 data sets used in well-established AutoML benchmarks.
  arXiv  Detail & Related papers  (2021-01-26T17:17:49Z)
• Process Discovery for Structured Program Synthesis [70.29027202357385]
  A core task in process mining is process discovery which aims to learn an accurate process model from event log data. In this paper, we propose to use (block-) structured programs directly as target process models. We develop a novel bottom-up agglomerative approach to the discovery of such structured program process models.
  arXiv  Detail & Related papers  (2020-08-13T10:33:10Z)
• Formal Synthesis of Lyapunov Neural Networks [61.79595926825511]
  We propose an automatic and formally sound method for synthesising Lyapunov functions. We employ a counterexample-guided approach where a numerical learner and a symbolic verifier interact to construct provably correct Lyapunov neural networks. Our method synthesises Lyapunov functions faster and over wider spatial domains than the alternatives, yet providing stronger or equal guarantees.
  arXiv  Detail & Related papers  (2020-03-19T17:21:02Z)
• An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoML [4.4181317696554325]
  A common claim of evolutionary computation methods is that they can achieve good results without the need for human intervention. We propose a near " parameter-free" genetic programming approach, which adapts the hyper parameter values throughout evolution without ever needing to be specified manually. We apply this to the area of automated machine learning (by extending TPOT) to produce pipelines which can effectively be claimed to be free from human input.
  arXiv  Detail & Related papers  (2020-01-28T05:44:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.