How to solve a classification problem using a cooperative tiling Multi-Agent System?
Adaptive Multi-Agent Systems (AMAS) transform dynamic problems into problems of local cooperation between agents. We present smapy, an ensemble based AMAS implementation for mobility prediction, whose agents are provided with machine learning models in addition to their cooperation rules. With a detailed methodology, we propose a framework to transform a classification problem into a cooperative tiling of the input variable space. We show that it is possible to use linear classifiers for online non-linear classification on three benchmark toy problems chosen for their different levels of linear separability, if they are integrated in a cooperative Multi-Agent structure. The results obtained show a significant improvement of the performance of linear classifiers in non-linear contexts in terms of classification accuracy and decision boundaries, thanks to the cooperative approach.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationSimilar Papers 제목 키워드 기반
A Simple Approach to Learning Unsupervised Multilingual Embeddings
Recent progress on unsupervised learning of cross-lingual embeddings in bilingual setting has given impetus to learning a shared embedding space for several languages without any supervision. A popular framework to solve…
Bilingual Lexicon InductionDependency ParsingDocument ClassificationWord Alignment+1INGEOTEC at SemEval-2020 Task 12: Multilingual Classification of Offensive Text
This paper describes our participation in OffensEval challenges for English, Arabic, Danish, Turkish, and Greek languages. We used several approaches, such as μTC, TextCategorization, and EvoMSA. Best results were achiev…
text-classificationText ClassificationShapleyLaw: A Game-Theoretic Approach to Multilingual Scaling Laws
In multilingual pretraining, the test loss of a pretrained model is heavily influenced by the proportion of each language in the pretraining data, namely the \textit{language mixture ratios}. Multilingual scaling laws ca…
Cross-Lingual TransferMiLMo:Minority Multilingual Pre-trained Language Model
Pre-trained language models are trained on large-scale unsupervised data, and they can fine-turn the model only on small-scale labeled datasets, and achieve good results. Multilingual pre-trained language models can be t…
ClassificationLanguage ModelingLanguage Modellingmodel+3Fixed-MAML for Few Shot Classification in Multilingual Speech Emotion Recognition
In this paper, we analyze the feasibility of applying few-shot learning to speech emotion recognition task (SER). The current speech emotion recognition models work exceptionally well but fail when then input is multilin…
Emotion RecognitionFew-Shot LearningGeneral ClassificationSpeech Emotion Recognition