scholarly journals Multimodal Data Evaluation for Classification Problems

2021 ◽  
Author(s):  
Daniela Moctezuma ◽  
Víctor Muníz ◽  
Jorge García
Keyword(s):  
Semantic Representation ◽  
User Profiling ◽  
Classification Model ◽  
Classification Problems ◽  
Multimodal Data ◽  
Lexical Representations ◽  
Pros And Cons ◽  
Main Input ◽  
Text Images ◽  
Media Data

Social media data is currently the main input to a wide variety of research works in many knowledge fields. This kind of data is generally multimodal, i.e., it contains different modalities of information such as text, images, video or audio, mainly. To deal with multimodal data to tackle a specific task could be very difficult. One of the main challenges is to find useful representations of the data, capable of capturing the subtle information that the users who generate that information provided, or even the way they use it. In this paper, we analysed the usage of two modalities of data, images, and text, both in a separate way and by combining them to address two classification problems: meme's classification and user profiling. For images, we use a textual semantic representation by using a pre-trained model of image captioning. Later, a text classifier based on optimal lexical representations was used to build a classification model. Interesting findings were found in the usage of these two modalities of data, and the pros and cons of using them to solve the two classification problems are also discussed.

scholarly journals Perceiving Residents’ Festival Activities Based on Social Media Data: A Case Study in Beijing, China

2021 ◽  
Vol 10 (7) ◽  
pp. 474
Author(s):  
Bingqing Wang ◽  
Bin Meng ◽  
Juan Wang ◽  
Siyu Chen ◽  
Jian Liu
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Topic Model ◽  
Central Area ◽  
Classification Model ◽  
Social Media Data ◽  
Ring Road ◽  
Different Types ◽  
Spatial Differences ◽  
Media Data

Social media data contains real-time expressed information, including text and geographical location. As a new data source for crowd behavior research in the era of big data, it can reflect some aspects of the behavior of residents. In this study, a text classification model based on the BERT and Transformers framework was constructed, which was used to classify and extract more than 210,000 residents’ festival activities based on the 1.13 million Sina Weibo (Chinese “Twitter”) data collected from Beijing in 2019 data. On this basis, word frequency statistics, part-of-speech analysis, topic model, sentiment analysis and other methods were used to perceive different types of festival activities and quantitatively analyze the spatial differences of different types of festivals. The results show that traditional culture significantly influences residents’ festivals, reflecting residents’ motivation to participate in festivals and how residents participate in festivals and express their emotions. There are apparent spatial differences among residents in participating in festival activities. The main festival activities are distributed in the central area within the Fifth Ring Road in Beijing. In contrast, expressing feelings during the festival is mainly distributed outside the Fifth Ring Road in Beijing. The research integrates natural language processing technology, topic model analysis, spatial statistical analysis, and other technologies. It can also broaden the application field of social media data, especially text data, which provides a new research paradigm for studying residents’ festival activities and adds residents’ perception of the festival. The research results provide a basis for the design and management of the Chinese festival system.

ASSOCIATIVE CLASSIFICATION OF MAMMOGRAMS BASED ON PARALLEL MINING OF IMAGE BLOCKS

2012 ◽  
Vol 24 (06) ◽  
pp. 513-524
Author(s):  
Mohsen Alavash Shooshtari ◽  
Keivan Maghooli ◽  
Kambiz Badie
Keyword(s):  
Association Rules ◽  
Classification Systems ◽  
Classification Model ◽  
Automated Classification ◽  
Associative Classification ◽  
Classification Problems ◽  
Association Rules Mining ◽  
Parallel Mining ◽  
Transactional Databases ◽  
Unique Decision

One of the main objectives of data mining as a promising multidisciplinary field in computer science is to provide a classification model to be used for decision support purposes. In the medical imaging domain, mammograms classification is a difficult diagnostic task which calls for development of automated classification systems. Associative classification, as a special case of association rules mining, has been adopted in classification problems for years. In this paper, an associative classification framework based on parallel mining of image blocks is proposed to be used for mammograms discrimination. Indeed, association rules mining is applied to a commonly used mammography image database to classify digital mammograms into three categories, namely normal, benign and malign. In order to do so, first images are preprocessed and then features are extracted from non-overlapping image blocks and discretized for rule discovery. Association rules are then discovered through parallel mining of transactional databases which correspond to the image blocks, and finally are used within a unique decision-making scheme to predict the class of unknown samples. Finally, experiments are conducted to assess the effectiveness of the proposed framework. Results show that the proposed framework proved successful in terms of accuracy, precision, and recall, and suggest that the framework could be used as the core of any future associative classifier to support mammograms discrimination.

scholarly journals Temporal patterns of words with and without suffixes in spontaneous Hungarian

2019 ◽  
Vol 10 (1) ◽  
pp. 53-70
Author(s):  
Mária Gósy ◽  
Ákos Gocsál
Keyword(s):  
Lexical Access ◽  
Semantic Representation ◽  
Temporal Patterns ◽  
Spontaneous Speech ◽  
Lexical Representations ◽  
Temporal Properties ◽  
Specific Factors

Temporal properties of words are defined by physiological, psychical, and language-specific factors. Lexical representations are assumed to be stored either in a morphologically decomposed form or in a conceptually non-decomposed form. We assumed that the duration of words with and without suffixes would refer to the route of their lexical access. Measured durations of Hungarian nouns with various lengths produced by 10 speakers in spontaneous utterances revealed significant differences, depending on the words’ morphological structures. Durations of monomorphemic nouns were shorter than those of multimorphemic nouns, irrespective of the number of syllables they contained. Our interpretation is that multimorphemic words are accessed decompositionally in spontaneous speech, meaning that stem activation of the semantic representation is followed by activation of one or more suffixes. Durational differences of monomorphemic and multimorphemic words were not stable across word lengths. The number of suffixes did not influence the words’ temporal patterns. Kokkuvõte. Mária Gósy ja Ákos Gocsál: Sufiksiga ja sufiksita sõnade ajaline struktuur spontaanses ungari keeles. Sõnade ajalised omadused sõltuvad füsioloogilistest, psühholoogilistest ja keelespetsiifilistest teguritest. Eelduste kohaselt on sõnad mentaalses leksikonis representeeritud kas morfeemideks analüüsituna või tervikmõistena. Uurimuses lähtuti eeldusest, et sufiksiga ja sufiksita sõnade kestus viitab sellele, kuidas juurdepääs neile toimub. Mõõdeti kümne kõneleja spontaansetes lausungites produtseeritud eri pikkusega ungari nimisõnade kestust. Ilmnes, et kestus sõltus oluliselt sõna morfoloogilisest ülesehitusest. Tüvisõnade kestus oli tuletiste omast lühem, sõltumata silpide arvust sõnas. Järelduseks saadi, et juurdepääs tuletistele toimub spontaanses kõnes osade kaupa: tüve semantilise representatsiooni aktiveerimisele järgneb sufiksi või sufiksite aktiveerimine. Tüvisõnade ja tuletiste kestuserinevused olid eri pikkusega sõnade puhul erinevad. Sufiksite arv sõna ajalist struktuuri ei mõjutanud. Märksõnad: kestus, nimisõnad, tüvisõnad ja tuletised, leksikaalne juurdepääs, spontaansed lausungid

scholarly journals Compact Belief Rule Base Learning for Classification with Evidential Clustering

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 443 ◽  
Author(s):  
Lianmeng Jiao ◽  
Xiaojiao Geng ◽  
Quan Pan
Keyword(s):  
Classification System ◽  
Optimization Procedure ◽  
Classification Model ◽  
Rule Base ◽  
Data Sets ◽  
Classification Problems ◽  
Ecm Algorithm ◽  
Rule Based ◽  
Belief Rule Base ◽  
Belief Function Theory

The belief rule-based classification system (BRBCS) is a promising technique for addressing different types of uncertainty in complex classification problems, by introducing the belief function theory into the classical fuzzy rule-based classification system. However, in the BRBCS, high numbers of instances and features generally induce a belief rule base (BRB) with large size, which degrades the interpretability of the classification model for big data sets. In this paper, a BRB learning method based on the evidential C-means clustering (ECM) algorithm is proposed to efficiently design a compact belief rule-based classification system (CBRBCS). First, a supervised version of the ECM algorithm is designed by means of weighted product-space clustering to partition the training set with the goals of obtaining both good inter-cluster separability and inner-cluster pureness. Then, a systematic method is developed to construct belief rules based on the obtained credal partitions. Finally, an evidential partition entropy-based optimization procedure is designed to get a compact BRB with a better trade-off between accuracy and interpretability. The key benefit of the proposed CBRBCS is that it can provide a more interpretable classification model on the premise of comparative accuracy. Experiments based on synthetic and real data sets have been conducted to evaluate the classification accuracy and interpretability of the proposal.

An Algorithm of Edge Detection Based on FSVM

2013 ◽  
Vol 321-324 ◽  
pp. 1046-1050
Author(s):  
Ai Ping Cai
Keyword(s):  
Target Identification ◽  
Main Idea ◽  
Detection Algorithm ◽  
Training Data ◽  
Classification Model ◽  
Detection Methods ◽  
Support Vector ◽  
Classification Problems ◽  
Tracking Motion ◽  
Image Edge

The support vector machine (SVM) has been shown to be an efficient approach for a variety of classification problems. It has also been widely used in target identification and tracking, motion analysis, image segmentation technology. Traditional detection methods mostly exist pseudo-edge and poor anti-noise capability. Under these circumstances, developing an efficient method is necessary. In this paper, we propose a new detection algorithm based on FSVM, the main idea is to train classified sample and give all training data a degree of membership, increase punishment to the wrong sub-sample. Then training and testing the FSVM classification model. Finally, extract edge of the image by using FSVM classification model. Experimental results show that the new algorithm can detect a clear image edge and have a good anti-noise nature.

scholarly journals A Comparative Study of Transactional and Semantic Approaches for Predicting Cascades on Twitter

2018 ◽  
Author(s):  
Yunwei Zhao ◽  
Can Wang ◽  
Chi-Hung Chi ◽  
Kwok-Yan Lam ◽  
Sen Wang
Keyword(s):  
Comparative Study ◽  
Information Diffusion ◽  
Large Scale ◽  
Semantic Features ◽  
Information Cascades ◽  
Social Media Data ◽  
Twitter Data ◽  
Pros And Cons ◽  
Media Data ◽  
Transactional Models

The availability of massive social media data has enabled the prediction of people’s future behavioral trends at an unprecedented large scale. Information cascades study on Twitter has been an integral part of behavior analysis. A number of methods based on the transactional features (such as keyword frequency) and the semantic features (such as sentiment) have been proposed to predict the future cascading trends. However, an in-depth understanding of the pros and cons of semantic and transactional models is lacking. This paper conducts a comparative study of both approaches in predicting information diffusion with three mechanisms: retweet cascade, url cascade, and hashtag cascade. Experiments on Twitter data show that the semantic model outperforms the transactional model, if the exterior pattern is less directly observable (i.e. hashtag cascade). When it becomes more directly observable (i.e. retweet and url cascades), the semantic method yet delivers approximate accuracy (i.e. url cascade) or even worse accuracy (i.e. retweet cascade). Further, we demonstrate that the transactional and semantic models are not independent, and the performance gets greatly enhanced when combining both.

scholarly journals Application of an Interpretable Classification Model on Early Folding Residues during Protein Folding

2018 ◽  
Author(s):  
Sebastian Bittrich ◽  
Marika Kaden ◽  
Christoph Leberecht ◽  
Florian Kaiser ◽  
Thomas Villmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Folding ◽  
Learning Strategies ◽  
Life Sciences ◽  
Classification Model ◽  
Classification Problems ◽  
Hydrophobic Residues ◽  
Imbalanced Classification ◽  
Fine Grained ◽  
Generalized Matrix

AbstractBackgroundMachine learning strategies are prominent tools for data analysis. Especially in life sciences, they have become increasingly important to handle the growing datasets collected by the scientific community. Meanwhile, algorithms improve in performance, but also gain complexity, and tend to neglect interpretability and comprehensiveness of the resulting models.ResultsGeneralized Matrix Learning Vector Quantization (GMLVQ) is a supervised, prototype-based machine learning method and provides comprehensive visualization capabilities not present in other classifiers which allow for a fine-grained interpretation of the data. In contrast to commonly used machine learning strategies, GMLVQ is well-suited for imbalanced classification problems which are frequent in life sciences. We present a Weka plug-in implementing GMLVQ. The feasibility of GMLVQ is demonstrated on a dataset of Early Folding Residues (EFR) that have been shown to initiate and guide the protein folding process. Using 27 features, an area under the receiver operating characteristic of 76.6% was achieved which is comparable to other state-of-the-art classifiers.ConclusionsThe application on EFR prediction demonstrates how an easy interpretation of classification models can promote the comprehension of biological mechanisms. The results shed light on the special features of EFR which were reported as most influential for the classification: EFR are embedded in ordered secondary structure elements and they participate in networks of hydrophobic residues. Visualization capabilities of GMLVQ are presented as we demonstrate how to interpret the results.

scholarly journals Penerapan Convolutional Neural Networks untuk Mesin Penerjemah Bahasa Daerah Minangkabau Berbasis Gambar

2021 ◽  
Vol 5 (6) ◽  
pp. 1153-1160
Author(s):  
Mayanda Mega Santoni ◽  
Nurul Chamidah ◽  
Desta Sandya Prasvita ◽  
Helena Nurramdhani Irmanda ◽  
Ria Astriratma ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Image Data ◽  
Classification Model ◽  
Distance Method ◽  
Regional Language ◽  
Translation Accuracy ◽  
Regional Languages ◽  
Text Images

One of efforts by the Indonesian people to defend the country is to preserve and to maintain the regional languages. The current era of modernity makes the regional language image become old-fashioned, so that most them are no longer spoken.  If it is ignored, then there will be a cultural identity crisis that causes regional languages to be vulnerable to extinction. Technological developments can be used as a way to preserve regional languages. Digital image-based artificial intelligence technology using machine learning methods such as machine translation can be used to answer the problems. This research will use Deep Learning method, namely Convolutional Neural Networks (CNN). Data of this research were 1300 alphabetic images, 5000 text images and 200 vocabularies of Minangkabau regional language. Alphabetic image data is used for the formation of the CNN classification model. This model is used for text image recognition, the results of which will be translated into regional languages. The accuracy of the CNN model is 98.97%, while the accuracy for text image recognition (OCR) is 50.72%. This low accuracy is due to the failure of segmentation on the letters i and j. However, the translation accuracy increases after the implementation of the Leveinstan Distance algorithm which can correct text classification errors, with an accuracy value of 75.78%. Therefore, this research has succeeded in implementing the Convolutional Neural Networks (CNN) method in identifying text in text images and the Leveinstan Distance method in translating Indonesian text into regional language texts.  

scholarly journals A New Method for Solving Supervised Data Classification Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Parvaneh Shabanzadeh ◽  
Rubiyah Yusof
Keyword(s):  
Mining Industry ◽  
Data Classification ◽  
Classification Performance ◽  
Classification Model ◽  
Selection Algorithm ◽  
Classification Problems ◽  
Nonsmooth Nonconvex Optimization ◽  
Derivative Free ◽  
Real World Datasets ◽  
New Feature

Supervised data classification is one of the techniques used to extract nontrivial information from data. Classification is a widely used technique in various fields, including data mining, industry, medicine, science, and law. This paper considers a new algorithm for supervised data classification problems associated with the cluster analysis. The mathematical formulations for this algorithm are based on nonsmooth, nonconvex optimization. A new algorithm for solving this optimization problem is utilized. The new algorithm uses a derivative-free technique, with robustness and efficiency. To improve classification performance and efficiency in generating classification model, a new feature selection algorithm based on techniques of convex programming is suggested. Proposed methods are tested on real-world datasets. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithms.

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