Term-weighting learning via genetic programming for text classification

Escalante, Hugo Jair; García-Limón, Mauricio A.; Morales-Reyes, Alicia; Graff, Mario; Montes-y-Gómez, Manuel; Morales, Eduardo F.; Martínez-Carranza, José

doi:10.1016/j.knosys.2015.03.025

Cited by 58 publications

(31 citation statements)

References 33 publications

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“…Cummins [12] proposes a method based on Genetic Programming to determine and evaluate several term weighting schemes for the vector space model. Escalante et al [13] present an approach to improve the performance of classical term-weighting schemes using genetic programming. Their approach outperforms standard schemes, based on an extensive experimental comparison.…”

Section: Related Workmentioning

confidence: 99%

An automated text categorization framework based on hyperparameter optimization

Téllez

Moctezuma

Miranda-Jiménez

et al. 2018

Knowledge-Based Systems

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A great variety of text tasks such as topic or spam identification, user profiling, and sentiment analysis can be posed as a supervised learning problem and tackle using a text classifier. A text classifier consists of several subprocesses, some of them are general enough to be applied to any supervised learning problem, whereas others are specifically designed to tackle a particular task, using complex and computational expensive processes such as lemmatization, syntactic analysis, etc. Contrary to traditional approaches, we propose a minimalistic and wide system able to tackle text classification tasks independent of domain and language, namely µTC. It is composed by some easy to implement text transformations, text representations, and a supervised learning algorithm. These pieces produce a competitive classifier even in the domain of informally written text. We provide a detailed description of µTC along with an extensive experimental comparison with relevant state-of-the-art methods. µTC was compared on 30 different datasets. Regarding accuracy, µTC obtained the best performance in 20 datasets while achieves competitive results in the remaining 10. The compared datasets include several problems like topic and polarity classification, spam detection, user profiling and authorship attribution. Furthermore, it is important to state that our approach allows the usage of the technology even without knowledge of machine learning and natural language processing.

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Section: Related Workmentioning

confidence: 99%

An automated text categorization framework based on hyperparameter optimization

Téllez

Moctezuma

Miranda-Jiménez

et al. 2018

Knowledge-Based Systems

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“…Genetic programming was applied to find new term-weighting schemes in work [6]. The schemes were used to improve classification performance.…”

Section: Evolutionary Computationmentioning

confidence: 99%

Combined Approach for Sentiment Analysis in Slovak Using a Dictionary Annotated by Particle Swarm Optimization

Mikula¹,

Machová²

2018

AEI

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Sentiment analysis in the minor languages, such as Slovak, using dictionary approach is a difficult task. It requires a lot of human effort and it is time-consuming to prepare a reliable source of information, especially good dictionary. We propose an approach which uses a biologically inspired algorithm to find optimal polarity values for sentimental words. It applies a swarm intelligence algorithms, standard Particle Swarm Optimization (PSO) and Bare-bones Particle Swarm Optimization (BBPSO), to replace a human annotator at the moment of dictionary creation. We created two dictionaries, which were annotated by the human annotator, PSO and BBPSO. These dictionaries were compared with the result that the versions annotated by PSO and BBPSO outperformed a human annotator. Then a combined approach was used to classify reviews that do not contain words from the dictionary. These reviews decrease the classification performance significantly. The combined approach implements machine learning method to build a model based on the reviews classified by the dictionary approach. The combined approach finally reduced a number of unclassified reviews from 18% and 40.2% to 0.3% and increased the macro-F1 measure from 0.694 and 0.495 to 0.865 and 0.841.

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“…Term-weighting learning with evolutionary algorithms has been studied within information retrieval and text categoriza tion domains [19], [20], [21]. In [19] the authors learn infor mation retrieval weighting schemes with genetic programming, they aim to combine a few primitives trying to maximize aver-age precision.…”

Section: The Bag Of Visual Words Representationmentioning

confidence: 99%

“…In [19] the authors learn infor mation retrieval weighting schemes with genetic programming, they aim to combine a few primitives trying to maximize aver-age precision. In [20], [21] authors use genetic programming for learning weighting schemes for text classification tasks. This work focuses on learning weighting schemes for computer vision tasks.…”

Section: The Bag Of Visual Words Representationmentioning

confidence: 99%

Improving bag of visual words representations with genetic programming

Martinez-Carraza

Escalera

Ponce-López

et al. 2015

2015 International Joint Conference on Neural Networks (IJCNN)

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The bag of visual words is a well established repre sentation in diverse computer vision problems. Ta king inspiration from the fields of text mining and retrieval, this representation has proved to be very effective in a large number of domains.In most cases, a standard term-frequency weighting scheme is considered for representing images and videos in computer vision. This is somewhat surprising, as there are many alternative ways of generating bag of words representations within the text processing community. This paper explores the use of alternative weighting schemes for landmark tasks in computer vision: image categorization and gesture recognition. We study the suitability of using well-known supervised and unsupervised weighting schemes for such tasks. More importantly, we devise a genetic program that learns new ways of representing images and videos under the bag of visual words representation. The proposed method learns to combine term-weighting primitives trying to maximize the classification performance. Experimental results are reported in standard image and video data sets showing the effectiveness of the proposed evolutionary algorithm.

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Term-weighting learning via genetic programming for text classification

Cited by 58 publications

References 33 publications

An automated text categorization framework based on hyperparameter optimization

An automated text categorization framework based on hyperparameter optimization

Combined Approach for Sentiment Analysis in Slovak Using a Dictionary Annotated by Particle Swarm Optimization

Improving bag of visual words representations with genetic programming

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