A New Feature Selection Method based on Intuitionistic Fuzzy Entropy to Categorize Text Documents

Selection of highly discriminative feature in text document plays a major challenging role in categorization. Feature selection is an important task that involves dimensionality reduction of feature matrix, which in turn enhances the performance of categorization. This article presents a new feature selection method based on Intuitionistic Fuzzy Entropy (IFE) for Text Categorization. Firstly, Intuitionistic Fuzzy C-Means (IFCM) clustering method is employed to compute the intuitionistic membership values. The computed intuitionistic membership values are used to estimate intuitionistic fuzzy entropy via Match degree. Further, features with lower entropy values are selected to categorize the text documents. To find the efficacy of the proposed method, experiments are conducted on three standard benchmark datasets using three classifiers. F-measure is used to assess the performance of the classifiers. The proposed method shows impressive results as compared to other well known feature selection methods. Moreover, Intuitionistic Fuzzy Set (IFS) property addresses the uncertainty limitations of traditional fuzzy set

Handwritten OCR for Indic Scripts: A Comprehensive Overview of Machine Learning and Deep Learning Techniques

The potential uses of cursive optical character recognition, commonly known as OCR, in a number of industries, particularly document digitization, archiving, even language preservation, have attracted a lot of interest lately. In the framework of optical character recognition (OCR), the goal of this research is to provide a thorough understanding of both cutting-edge methods and the unique difficulties presented by Indic scripts. A thorough literature search was conducted in order to conduct this study, during which time relevant publications, conference proceedings, and scientific files were looked for up to the year 2023. As a consequence of the inclusion criteria that were developed to concentrate on studies only addressing Handwritten OCR on Indic scripts, 53 research publications were chosen as the process's outcome. The review provides a thorough analysis of the methodology and approaches employed in the chosen study. Deep neural networks, conventional feature-based methods, machine learning techniques, and hybrid systems have all been investigated as viable answers to the problem of effectively deciphering Indian scripts, because they are famously challenging to write. To operate, these systems require pre-processing techniques, segmentation schemes, and language models. The outcomes of this methodical examination demonstrate that despite the fact that Hand Scanning for Indic script has advanced significantly, room still exists for advancement. Future research could focus on developing trustworthy models that can handle a range of writing styles and enhance accuracy using less-studied Indic scripts. This profession may advance with the creation of collected datasets and defined standards

Enhancing feature selection with a novel hybrid approach incorporating genetic algorithms and swarm intelligence techniques

Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance

Abordagens multivariadas para seleção de variáveis com vistas à classificação e predição de propriedades de amostras

A seleção de variáveis é uma etapa importante para a análise de dados, visto que identifica os subconjuntos de variáveis mais informativas para a construção de modelos precisos de classificação e predição. Além disso, a seleção de variáveis facilita a interpretação e análise dos modelos obtidos, potencialmente reduzindo o tempo computacional de geração dos modelos e o custo/tempo para obtenção das amostras. Neste contexto, a presente tese apresenta proposições inovadoras de abordagens com vistas à seleção de variáveis para classificação e predição de propriedades de amostras de produtos diversos. Tais abordagens são abordadas em três artigos apresentados nesta tese, com intuito de melhorar a precisão dos modelos de classificação e predição em diferentes áreas. No primeiro artigo, integram-se índices de importância de variáveis a sistemáticas de classificação hierárquica para categorizar amostras de espumantes de acordo com seu país de origem. No segundo artigo, para selecionar as variáveis mais informativas para a predição de amostras via PLS, propõe-se um índice de importância de variáveis baseado na Lei de Lambert-Beer combinado a um processo iterativo de seleção do tipo forward. Por fim, o terceiro artigo utilizou cluster de variáveis espectrais e índice de importância para selecionar as variáveis que produzem modelos de predição mais consistentes. Em todos os artigos dessa tese, os resultados obtidos pelos métodos propostos foram superiores quando comparados a outros métodos tradicionais da literatura voltados à identificação das variáveis mais informativas.Variable selection is an important step in data analysis, since it identifies the most informative subsets of variables for build accurate classification and prediction models. In addition, variable selection improves the interpretation and analysis of obtained models, reduces the computational time to build models and reduces the obtained samples costs. In this context, this thesis presents propositions for a variable selection method aiming to classifying and predicting sample properties. Such methods are presented in three papers in this thesis, in order to improve the classification and prediction accuracy in different areas. In first paper, we applied variable importance index coupled with a hierarchical classification technique to identify the country of origin of sparkling wines. In second paper, to select the most informative variables for prediction, a variable improtance index was built based on Lambert-Beer law and an iterative forward process was performed. Finally, in third paper was used clustering of variables and variable importance index to select the variables that produce more consistent prediction models. In all papers of this thesis, when conpared to other traditional methods, our proposition obtained better results

Multi-objective of wind-driven optimization as feature selection and clustering to enhance text clustering

Text Clustering consists of grouping objects of similar categories. The initial centroids influence operation of the system with the potential to become trapped in local optima. The second issue pertains to the impact of a huge number of features on the determination of optimal initial centroids. The problem of dimensionality may be reduced by feature selection. Therefore, Wind Driven Optimization (WDO) was employed as Feature Selection to reduce the unimportant words from the text. In addition, the current study has integrated a novel clustering optimization technique called the WDO (Wasp Swarm Optimization) to effectively determine the most suitable initial centroids. The result showed the new meta-heuristic which is WDO was employed as the multi-objective first time as unsupervised Feature Selection (WDOFS) and the second time as a Clustering algorithm (WDOC). For example, the WDOC outperformed Harmony Search and Particle Swarm in terms of F-measurement by 93.3%; in contrast, text clustering's performance improves 0.9% because of using suggested clustering on the proposed feature selection. With WDOFS more than 50 percent of features have been removed from the other examination of features. The best result got the multi-objectives with F-measurement 98.3%

Improved relative discriminative criterion using rare and informative terms and ringed seal search-support vector machine techniques for text classification

Classification has become an important task for automatically classifying the documents to their respective categories. For text classification, feature selection techniques are normally used to identify important features and to remove irrelevant, and noisy features for minimizing the dimensionality of feature space. These techniques are expected particularly to improve efficiency, accuracy, and comprehensibility of the classification models in text labeling problems. Most of the feature selection techniques utilize document and term frequencies to rank a term. Existing feature selection techniques (e.g. RDC, NRDC) consider frequently occurring terms and ignore rarely occurring terms count in a class. However, this study proposes the Improved Relative Discriminative Criterion (IRDC) technique which considers rarely occurring terms count. It is argued that rarely occurring terms count are also meaningful and important as frequently occurring terms in a class. The proposed IRDC is compared to the most recent feature selection techniques RDC and NRDC. The results reveal significant improvement by the proposed IRDC technique for feature selection in terms of precision 27%, recall 30%, macro-average 35% and micro- average 30%. Additionally, this study also proposes a hybrid algorithm named: Ringed Seal Search-Support Vector Machine (RSS-SVM) to improve the generalization and learning capability of the SVM. The proposed RSS-SVM optimizes kernel and penalty parameter with the help of RSS algorithm. The proposed RSS-SVM is compared to the most recent techniques GA-SVM and CS-SVM. The results show significant improvement by the proposed RSS-SVM for classification in terms of accuracy 18.8%, recall 15.68%, precision 15.62% and specificity 13.69%. In conclusion, the proposed IRDC has shown better performance as compare to existing techniques because its capability in considering rare and informative terms. Additionally, the proposed RSS- SVM has shown better performance as compare to existing techniques because it has capability to improve balance between exploration and exploitation

Alternative Relative Discrimination Criterion Feature Ranking Technique for Text Classification

The use of text data with high dimensionality affects classifier performance. Therefore, efficient feature selection (FS) is necessary to reduce dimensionality. In text classification challenges, FS algorithms based on a ranking approach are employed to improve the classification performance. To rank terms, most feature ranking algorithms, such as the Relative Discrimination Criterion (RDC) and Improved Relative Discrimination Criterion (IRDC), use document frequency (DF) and term frequency (TF). TF accepts the actual values of a term with frequently and rarely occurring terms used in existing feature ranking algorithms. However, these algorithms focus on the number of terms in a document rather than the number of terms in the category. In this research, an alternative method to RDC, called Alternative Relative Discrimination Criterion (ARDC) was proposed, which aims to improve the accuracy and effectiveness of RDC feature ranking. Specifically, ARDC is designed to identify terms commonly occurring in the positive class. The results obtained were compared to the existing RDC methods, which are RDC and IRDC, and standard benchmarking functions such as Information Gain (IG), Pearson Correlation Coefficient (PCC), and ReliefF. The experimental results reveal that using the suggested ARDC on the Reuters21578, 20newsgroup, and TDT2 datasets provides better performance in terms of precision, recall, f-measure, and accuracy when employing well-known classifiers such as multinomial naïve Bayes (MNB), Support Vector Machine (SVM), Multilayer perceptron (MLP), k-nearest neighbor (KNN), and decision tree (DT). Another experiment was performed to validate the proposed technique, which aims to showcase the novelty of the ARDC approach. The experiment utilized the 20newsgroup dataset and employed the Relevant-Based Feature Ranking (RBFR) technique. Naïve Bayes (NB), Random Forest (RF) and Logistic Regression (LR) classifiers were used in this experiment to demonstrate the effectiveness of the suggested ARDC

A deep learning framework for contingent liabilities risk management : predicting Brazilian labor court decisions

Estimar o resultado de um processo em litígio é crucial para muitas organizações. Uma aplicação específica são os "Passivos Contingenciais", que se referem a passivos que podem ou não ocorrer dependendo do resultado de um processo judicial em litígio. A metodologia tradicional para estimar essa probabilidade baseia-se na opinião de um advogado quem determina a possibilidade de um processo judicial ser perdido a partir de uma avaliação quantitativa. Esta tese apresenta a um modelo matemático baseado numa arquitetura de Deep Learning cujo objetivo é estimar a probabilidade de ganho ou perda de um processo de litígio, principalmente para ser utilizada na estimação de Passivos Contingenciais. A arquitetura, diferentemente do método tradicional, oferece um maior grau de confiança ao prever o resultado de um processo legal em termos de probabilidade e com um tempo de processamento de segundos. Além do resultado primário, a arquitetura estima uma amostra dos casos mais semelhantes ao processo estimado, que servem de apoio para a realização de estratégias de litígio. Nossa arquitetura foi testada em duas bases de dados de processos legais: (1) o Tribunal Europeu de Direitos Humanos (ECHR) e (2) o 4º Tribunal Regional do Trabalho brasileiro (4TRT). Ela estimou de acordo com nosso conhecimento, o melhor desempenho já publicado (precisão = 0,906) na base de dados da ECHR, uma coleção amplamente utilizada de processos legais, e é o primeiro trabalho a aplicar essa metodologia em um tribunal de trabalho brasileiro. Os resultados mostram que a arquitetura é uma alternativa adequada a ser utilizada contra o método tradicional de estimação do desfecho de um processo em litígio realizado por advogados. Finalmente, validamos nossos resultados com especialistas que confirmaram as possibilidades promissoras da arquitetura. Assim, nos incentivamos os académicos a continuar desenvolvendo pesquisas sobre modelagem matemática na área jurídica, pois é um tema emergente com um futuro promissor e aos usuários a utilizar ferramentas baseadas como a desenvolvida em nosso trabalho, pois fornecem vantagens substanciais em termos de precisão e velocidade sobre os métodos convencionais.Estimating the likely outcome of a litigation process is crucial for many organizations. A specific application is the “Contingents Liabilities,” which refers to liabilities that may or may not occur depending on the result of a pending litigation process (lawsuit). The traditional methodology for estimating this likelihood is based on the opinion from the lawyer’s experience which is based on a qualitative appreciation. This dissertation presents a mathematical modeling framework based on a Deep Learning architecture that estimates the probability outcome of a litigation process (accepted & not accepted) with a particular use on Contingent Liabilities. The framework offers a degree of confidence by describing how likely an event will occur in terms of probability and provides results in seconds. Besides the primary outcome, it offers a sample of the most similar cases to the estimated lawsuit that serve as support to perform litigation strategies. We tested our framework in two litigation process databases from: (1) the European Court of Human Rights (ECHR) and (2) the Brazilian 4th regional labor court. Our framework achieved to our knowledge the best-published performance (precision = 0.906) on the ECHR database, a widely used collection of litigation processes, and it is the first to be applied in a Brazilian labor court. Results show that the framework is a suitable alternative to be used against the traditional method of estimating the verdict outcome from a pending litigation performed by lawyers. Finally, we validated our results with experts who confirmed the promising possibilities of the framework. We encourage academics to continue developing research on mathematical modeling in the legal area as it is an emerging topic with a promising future and practitioners to use tools based as the proposed, as they provides substantial advantages in terms of accuracy and speed over conventional methods