Pattern Discovery and Validation Using Scientific Research Methods
- URL: http://arxiv.org/abs/2107.06065v1
- Date: Sun, 11 Jul 2021 16:11:56 GMT
- Title: Pattern Discovery and Validation Using Scientific Research Methods
- Authors: Dirk Riehle, Nikolay Harutyunyan, Ann Barcomb
- Abstract summary: This article shows how to use scientific research methods for the purpose of pattern discovery and validation.
We present a specific approach, called the handbook method, that uses the qualitative survey, action research, and case study research for pattern discovery and evaluation.
- Score: 3.1798318618973362
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Pattern discovery, the process of discovering previously unrecognized
patterns, is often performed as an ad-hoc process with little resulting
certainty in the quality of the proposed patterns. Pattern validation, the
process of validating the accuracy of proposed patterns, remains dominated by
the simple heuristic of "the rule of three". This article shows how to use
established scientific research methods for the purpose of pattern discovery
and validation. We present a specific approach, called the handbook method,
that uses the qualitative survey, action research, and case study research for
pattern discovery and evaluation, and we discuss the underlying principle of
using scientific methods in general. We evaluate the handbook method using
three exploratory studies and demonstrate its usefulness.
Related papers
- Towards Understanding Sensitive and Decisive Patterns in Explainable AI: A Case Study of Model Interpretation in Geometric Deep Learning [18.408342615833185]
This research focuses on distinguishing between two critical data patterns -- sensitive patterns (model-related) and decisive patterns (task-related)
We compare the effectiveness of two main streams of interpretation methods: post-hoc methods and self-interpretable methods.
Our findings indicate that post-hoc methods tend to provide interpretations better aligned with sensitive patterns, whereas certain self-interpretable methods exhibit strong and stable performance in detecting decisive patterns.
arXiv Detail & Related papers (2024-06-30T22:59:15Z) - Model-Free Active Exploration in Reinforcement Learning [53.786439742572995]
We study the problem of exploration in Reinforcement Learning and present a novel model-free solution.
Our strategy is able to identify efficient policies faster than state-of-the-art exploration approaches.
arXiv Detail & Related papers (2024-06-30T19:00:49Z) - Guiding Principles for Using Mixed Methods Research in Software Engineering [51.22583433491887]
Mixed methods research is often used in software engineering, but researchers outside of the social or human sciences often lack experience when using these designs.
This paper provides guiding principles and advice on how to design mixed method research.
arXiv Detail & Related papers (2024-04-09T04:34:25Z) - Generative AI-Based Text Generation Methods Using Pre-Trained GPT-2 Model [2.6320841968362645]
This work delved into the realm of automatic text generation, exploring a variety of techniques ranging from traditional deterministic approaches to more modern methods.
Through analysis of greedy search, beam search, top-k sampling, top-p sampling, contrastive searching, and locally typical searching, this work has provided valuable insights into the strengths, weaknesses, and potential applications of each method.
arXiv Detail & Related papers (2024-04-02T09:49:53Z) - Enhancing AI Research Paper Analysis: Methodology Component Extraction
using Factored Transformer-based Sequence Modeling Approach [10.060305577353633]
We propose a factored approach to sequence modeling, which leverages a broad-level category information of methodology domains.
We conduct experiments following a simulated chronological setup (newer methodologies not seen during the training process)
Our experiments demonstrate that the factored approach outperforms state-of-the-art baselines by margins of up to 9.257% for the methodology extraction task with the few-shot setup.
arXiv Detail & Related papers (2023-11-05T16:33:35Z) - A Comparative Study on Unsupervised Anomaly Detection for Time Series:
Experiments and Analysis [28.79393419730138]
Time series anomaly detection is often essential to enable reliability and safety.
Many recent studies target anomaly detection for time series data.
We introduce for data, methods, and evaluation strategies.
We systematically evaluate and compare state-of-the-art traditional as well as deep learning techniques.
arXiv Detail & Related papers (2022-09-10T10:44:25Z) - The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set
Methods [86.39044549664189]
Anomaly detection algorithms for feature-vector data identify anomalies as outliers, but outlier detection has not worked well in deep learning.
This paper proposes the Familiarity Hypothesis that these methods succeed because they are detecting the absence of familiar learned features rather than the presence of novelty.
The paper concludes with a discussion of whether familiarity detection is an inevitable consequence of representation learning.
arXiv Detail & Related papers (2022-03-04T18:32:58Z) - Predictive machine learning for prescriptive applications: a coupled
training-validating approach [77.34726150561087]
We propose a new method for training predictive machine learning models for prescriptive applications.
This approach is based on tweaking the validation step in the standard training-validating-testing scheme.
Several experiments with synthetic data demonstrate promising results in reducing the prescription costs in both deterministic and real models.
arXiv Detail & Related papers (2021-10-22T15:03:20Z) - Deep Learning Schema-based Event Extraction: Literature Review and
Current Trends [60.29289298349322]
Event extraction technology based on deep learning has become a research hotspot.
This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
arXiv Detail & Related papers (2021-07-05T16:32:45Z) - Evaluating the Disentanglement of Deep Generative Models through
Manifold Topology [66.06153115971732]
We present a method for quantifying disentanglement that only uses the generative model.
We empirically evaluate several state-of-the-art models across multiple datasets.
arXiv Detail & Related papers (2020-06-05T20:54:11Z)
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.