Discovering Categorical Main and Interaction Effects Based on Association Rule Mining

• URL: http://arxiv.org/abs/2104.04728v1
• Date: Sat, 10 Apr 2021 10:13:07 GMT
• Title: Discovering Categorical Main and Interaction Effects Based on Association Rule Mining
• Authors: Qiuqiang Lin, Chuanhou Gao
• Abstract summary: We use association rules to select features and their interactions, then modify the algorithm for several practical concerns. We analyze the computational complexity of the proposed algorithm to show its efficiency.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the growing size of data sets, feature selection becomes increasingly important. Taking interactions of original features into consideration will lead to extremely high dimension, especially when the features are categorical and one-hot encoding is applied. This makes it more worthwhile mining useful features as well as their interactions. Association rule mining aims to extract interesting correlations between items, but it is difficult to use rules as a qualified classifier themselves. Drawing inspiration from association rule mining, we come up with a method that uses association rules to select features and their interactions, then modify the algorithm for several practical concerns. We analyze the computational complexity of the proposed algorithm to show its efficiency. And the results of a series of experiments verify the effectiveness of the algorithm.

Related papers

• Efficient Differentiable Discovery of Causal Order [14.980926991441342]
  Intersort is a score-based method to discover causal order of variables. We reformulate Intersort using differentiable sorting and ranking techniques. Our work opens the door to efficiently incorporating regularization for causal order into the training of differentiable models.
  arXiv  Detail & Related papers  (2024-10-11T13:11:55Z)
• Relation-aware Ensemble Learning for Knowledge Graph Embedding [68.94900786314666]
  We propose to learn an ensemble by leveraging existing methods in a relation-aware manner. exploring these semantics using relation-aware ensemble leads to a much larger search space than general ensemble methods. We propose a divide-search-combine algorithm RelEns-DSC that searches the relation-wise ensemble weights independently.
  arXiv  Detail & Related papers  (2023-10-13T07:40:12Z)
• Association Rules Mining with Auto-Encoders [5.175050215292647]
  We present an auto-encoder solution to mine association rule called ARM-AE. Our algorithm discovers high support and confidence rule set and has a better execution time than classical methods.
  arXiv  Detail & Related papers  (2023-04-26T17:55:44Z)
• Scalable Batch Acquisition for Deep Bayesian Active Learning [70.68403899432198]
  In deep active learning, it is important to choose multiple examples to markup at each step. Existing solutions to this problem, such as BatchBALD, have significant limitations in selecting a large number of examples. We present the Large BatchBALD algorithm, which aims to achieve comparable quality while being more computationally efficient.
  arXiv  Detail & Related papers  (2023-01-13T11:45:17Z)
• Personalized Decentralized Multi-Task Learning Over Dynamic Communication Graphs [59.96266198512243]
  We propose a decentralized and federated learning algorithm for tasks that are positively and negatively correlated. Our algorithm uses gradients to calculate the correlations among tasks automatically, and dynamically adjusts the communication graph to connect mutually beneficial tasks and isolate those that may negatively impact each other. We conduct experiments on a synthetic Gaussian dataset and a large-scale celebrity attributes (CelebA) dataset.
  arXiv  Detail & Related papers  (2022-12-21T18:58:24Z)
• Cambrian Explosion Algorithm for Multi-Objective Association Rules Mining [5.175050215292647]
  Association rule mining is one of the most studied research fields of data mining. We compare the performances of state-of-the-art meta-heuristics on the association rule mining problem. We propose a new algorithm designed to mine rules efficiently from massive datasets by exploring a large variety of solutions.
  arXiv  Detail & Related papers  (2022-11-23T08:34:05Z)
• Towards Correlated Sequential Rules [4.743965372344134]
  High-utility sequential rule mining (HUSRM) is designed to explore the confidence or probability of predicting the occurrence of consequence sequential patterns. The existing algorithm, known as HUSRM, is limited to extracting all eligible rules while neglecting the correlation between the generated sequential rules. We propose a novel algorithm called correlated high-utility sequential rule miner (CoUSR) to integrate the concept of correlation into HUSRM.
  arXiv  Detail & Related papers  (2022-10-27T17:27:23Z)
• Deep Reinforcement Learning of Graph Matching [63.469961545293756]
  Graph matching (GM) under node and pairwise constraints has been a building block in areas from optimization to computer vision. We present a reinforcement learning solver for GM i.e. RGM that seeks the node correspondence between pairwise graphs. Our method differs from the previous deep graph matching model in the sense that they are focused on the front-end feature extraction and affinity function learning.
  arXiv  Detail & Related papers  (2020-12-16T13:48:48Z)
• A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention [96.77554122595578]
  We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
  arXiv  Detail & Related papers  (2020-06-22T08:35:58Z)
• A Hybrid Approach to Enhance Pure Collaborative Filtering based on Content Feature Relationship [0.17188280334580192]
  We introduce a novel method to extract the implicit relationship between content features using a sort of well-known methods from the natural language processing domain, namely Word2Vec. Next, we propose a novel content-based recommendation system that employs the relationship to determine vector representations for items. Our evaluation results demonstrate that it can predict the preference a user would have for a set of items as good as pure collaborative filtering.
  arXiv  Detail & Related papers  (2020-05-17T02:20:45Z)
• Optimal Clustering from Noisy Binary Feedback [75.17453757892152]
  We study the problem of clustering a set of items from binary user feedback. We devise an algorithm with a minimal cluster recovery error rate. For adaptive selection, we develop an algorithm inspired by the derivation of the information-theoretical error lower bounds.
  arXiv  Detail & Related papers  (2019-10-14T09:18:26Z)

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.