Meta-learning and multi-task learning in data mining——Research on multi-task learning model and meta-learning algorithm based on Python

Author: Zen and the Art of Computer Programming

1 Introduction

Data mining is an interdisciplinary subject based on statistics and computer science. It involves computer systems extracting valuable information from various information sources and applying it to tasks such as analysis, decision-making or prediction. It has a wide range of applications in different fields. Natural language processing (NLP) and text classification are an important branch of data mining, through automatic classification, processing and analysis of text to obtain key information that is conducive to decision-making. A key issue in NLP is how to effectively understand textual information while maintaining accuracy. With the improvement of computing power, machine learning technology has made great progress, especially in the field of text classification, machine learning algorithms such as naive Bayesian, support vector machine, neural network, etc. can achieve high accuracy. However, due to the complexity, heterogeneity, incompleteness, etc. of the text information itself, there are still some limitations. For example, for text classification tasks, features such as word order, grammatical structure, and semantic information are often more important, while traditional machine learning methods cannot take these features into account. In addition, the multi-label text classification problem is also a prominent problem. In response to the above two problems, in recent years, researchers have proposed many new machine learning techniques such as meta-learning, multi-task learning, and ensemble learning, trying to use data from multiple tasks to improve the final performance. This article will mainly discuss the application of meta-learning and multi-task learning techniques in NLP.

2. Meta-learning

Meta-learning is to provide a training sample set for the machine learning model instead of individual samples. The training sample set consists of data generated by various tasks. Meta-learning aims to learn a model such that it can capture the commonalities and differences of data from different tasks, so it can effectively deal with the data imbalance problem in multi-task learning. Meta-learning can solve the following problems:

1. Limited training data: When given a machine learning task, there may only be a small amount of training data available. At this point, collecting, labeling and using more data for training can help improve the performance of the model.
2. Model capacity limitations: In the real world, large amounts of data do not guarantee good models, and