New Trends in Software Methodologies, Tools and Techniques: Proceedings of Lyee-W02, 2002
The paper deals with the modelling of Lyee user requirements and guidelines to support their capt... more The paper deals with the modelling of Lyee user requirements and guidelines to support their capture. The Sorbonne contribution to the Lyee collaborative project aims to reduce the software development cycle to two explicit steps, requirements engineering and code generation by coupling the code generation features of LyeeALL with an interface to capture user requirements. The paper presents a 2-layer meta-model relating the set of concepts to capture user requirements to the set of concepts for the formulation of software requirements that are the input of the LyeeALL generation mechanism. It exemplifies the concepts with example and introduces the guidance support for capturing these user centric requirements.
Supervised learning requires a sufficient training dataset which includes all label. However, the... more Supervised learning requires a sufficient training dataset which includes all label. However, there are cases that some class is not in the training data. Zero-Shot Learning (ZSL) is the task of predicting class that is not in the training data(target class). The existing ZSL method is done for image data. However, the zero-shot problem should happen to every data type. Hence, considering ZSL for other data types is required. In this paper, we propose the cluster-based ZSL method, which is a baseline method for multivariate binary classification problems. The proposed method is based on the assumption that if data is far from training data, the data is considered as target class. In training, clustering is done for training data. In prediction, the data is determined belonging to a cluster or not. If data does not belong to a cluster, the data is predicted as target class. The proposed method is evaluated and demonstrated using the KEEL dataset.
Word2vec has been proven to facilitate various Natural Language Processing (NLP) tasks. We suppos... more Word2vec has been proven to facilitate various Natural Language Processing (NLP) tasks. We suppose that it could separate the vector space of word2vec into positive and negative. Hence, word2vec can be applied to Sentiment Analysis tasks. In our previous research, we proposed the word embeddings (WEMB) based Sentence-level Sentiment Analysis method. Word’s vectors from WEMB are utilized to calculate the sentence vector. Training of the classification model is done using sentence vector and the polarity. After training, the model predicts the polarity of the unlabeled sentence. However, the sentence vector was insufficient because the method treats all words with the same weight for calculating a sentence vector. In this paper, we propose a method to solve this problem. We consider word weight according to their importance for calculating sentence vector. The proposed method is compared with the method without word importance, and the accuracy is improved. However, there is still a g...
Imbalanced data classification is an important issue in machine learning. To solve the data imbal... more Imbalanced data classification is an important issue in machine learning. To solve the data imbalance problem, rebalance algorithms are utilized. However, the rebalance algorithm has a lot of problems. Hence, the specific classification algorithm without a rebalancing algorithm is required. In this paper, we apply the zero-shot classifier to imbalance problem. The zero-shot classifier works despite the number of minority class is zero. Hence, the zero-shot classifier should also work in weaker data imbalance problems. We utilize the cluster-based zero-shot learning algorithm to do imbalanced data classification. The proposed method is evaluated using an imbalanced-learn dataset and compared with Decision Tree, K-Nearest Neighbor, and Over-sampling methods.
A multiple attribute decision making three-way model for intuitionistic fuzzy numbers
International Journal of Approximate Reasoning
Abstract In order to use three-way decision (TWD) to solve multiple attribute decision making (MA... more Abstract In order to use three-way decision (TWD) to solve multiple attribute decision making (MADM) problems, in this article, a new TWD model with intuitionistic fuzzy numbers (IFNs) is proposed. First of all, we define the relative loss functions to demonstrate some features of loss functions in TWDs, which is the basis for future research. Then, based on the correlation between the loss functions and the IFNs, we get the relative loss functions based on IFNs. At the same time, the classification rules of the TWDs are discussed from different viewpoints, including the thresholds and their properties. Aiming at MADM problems with unreasonable values, a new integrated method of relative loss functions is established to obtain a fairer loss integration result of alternatives. In addition, considering that there are no decision attributes and only condition attributes in MADM, we use grey relational degree to calculate the condition probability. In the end, a novel TWD model is proposed to solve MADM problems with IFNs, and a practical example on selecting suppliers is used to demonstrate its effectiveness and practicability.
Question Classification (QC) is of primary importance in question answering systems, since it ena... more Question Classification (QC) is of primary importance in question answering systems, since it enables extraction of the correct answer type. State-of-the-art solutions for short text classification obtained remarkable results by Convolutional Neural Networks (CNNs). However, implementing such models requires choices, usually based on subjective experience, or on rare works comparing different settings for general text classification, while peculiar solutions should be individuated for QC task, depending on language and on dataset size. Therefore, this work aims at suggesting best practices for QC using CNNs. Different datasets were employed: (i) A multilingual set of labelled questions to evaluate the dependence of optimal settings on language; (ii) a large, widely used dataset for validation and comparison. Numerous experiments were executed, to perform a multivariate analysis, for evaluating statistical significance and influence on QC performance of all the factors (regarding tex...
Many nations and world regions have suffered on different aspects and fronts from the novel Covid... more Many nations and world regions have suffered on different aspects and fronts from the novel Covid-19. The Scientific community has done great challenges to tackle this virus and its devastating pandemic behavior. Applied Intelligence was part of such a community and has opened the special issue calls. These special-issue calls have established an avenue disseminating research outcomes that have contributed to understanding and taking a lead on the pandemic by pointing out ways to dominate the virus more than it dominates us. Applied Intelligence has received a big number of submissions in large as 480 articles. The journal has done extensive reviews weighing these submissions to differentiate the best in terms of quality, novelty, and its technical contribution on research and development on novel Covid-19. This special issue has incubated 25 articles gathered in Covid-19 special issue in Applied Intelligence; each has been reviewed by at least three expert reviewers and then selected based on the quality of its contribution on the subject. A few articles Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Intuitionistic fuzzy multiple attribute decision making deals with the issue of ranking alternati... more Intuitionistic fuzzy multiple attribute decision making deals with the issue of ranking alternatives based on the decision information quantified in terms of intuitionistic fuzzy values. Lexicographic orders can serve as efficient and indispensable tools for comparing intuitionistic fuzzy values. This paper introduces a number of lexicographic orders by means of several measures such as the membership, non-membership, score, accuracy and expectation score functions. Some equivalent characterizations and illustrative examples are provided, from which the relationships among these lexicographic orders are ascertained. We also propose three different compatible properties of preorders with respect to the algebraic sum and scalar product operations of intuitionistic fuzzy values, and apply them to the investigation of compatible properties of various lexicographic orders. In addition, a benchmark problem regarding risk investment is further explored to give a comparative analysis of dif...
New Trends in Software Methodologies, Tools and Techniques: Proceedings of Lyee-W02, 2002
The paper deals with the modelling of Lyee user requirements and guidelines to support their capt... more The paper deals with the modelling of Lyee user requirements and guidelines to support their capture. The Sorbonne contribution to the Lyee collaborative project aims to reduce the software development cycle to two explicit steps, requirements engineering and code generation by coupling the code generation features of LyeeALL with an interface to capture user requirements. The paper presents a 2-layer meta-model relating the set of concepts to capture user requirements to the set of concepts for the formulation of software requirements that are the input of the LyeeALL generation mechanism. It exemplifies the concepts with example and introduces the guidance support for capturing these user centric requirements.
Supervised learning requires a sufficient training dataset which includes all label. However, the... more Supervised learning requires a sufficient training dataset which includes all label. However, there are cases that some class is not in the training data. Zero-Shot Learning (ZSL) is the task of predicting class that is not in the training data(target class). The existing ZSL method is done for image data. However, the zero-shot problem should happen to every data type. Hence, considering ZSL for other data types is required. In this paper, we propose the cluster-based ZSL method, which is a baseline method for multivariate binary classification problems. The proposed method is based on the assumption that if data is far from training data, the data is considered as target class. In training, clustering is done for training data. In prediction, the data is determined belonging to a cluster or not. If data does not belong to a cluster, the data is predicted as target class. The proposed method is evaluated and demonstrated using the KEEL dataset.
Word2vec has been proven to facilitate various Natural Language Processing (NLP) tasks. We suppos... more Word2vec has been proven to facilitate various Natural Language Processing (NLP) tasks. We suppose that it could separate the vector space of word2vec into positive and negative. Hence, word2vec can be applied to Sentiment Analysis tasks. In our previous research, we proposed the word embeddings (WEMB) based Sentence-level Sentiment Analysis method. Word’s vectors from WEMB are utilized to calculate the sentence vector. Training of the classification model is done using sentence vector and the polarity. After training, the model predicts the polarity of the unlabeled sentence. However, the sentence vector was insufficient because the method treats all words with the same weight for calculating a sentence vector. In this paper, we propose a method to solve this problem. We consider word weight according to their importance for calculating sentence vector. The proposed method is compared with the method without word importance, and the accuracy is improved. However, there is still a g...
Imbalanced data classification is an important issue in machine learning. To solve the data imbal... more Imbalanced data classification is an important issue in machine learning. To solve the data imbalance problem, rebalance algorithms are utilized. However, the rebalance algorithm has a lot of problems. Hence, the specific classification algorithm without a rebalancing algorithm is required. In this paper, we apply the zero-shot classifier to imbalance problem. The zero-shot classifier works despite the number of minority class is zero. Hence, the zero-shot classifier should also work in weaker data imbalance problems. We utilize the cluster-based zero-shot learning algorithm to do imbalanced data classification. The proposed method is evaluated using an imbalanced-learn dataset and compared with Decision Tree, K-Nearest Neighbor, and Over-sampling methods.
A multiple attribute decision making three-way model for intuitionistic fuzzy numbers
International Journal of Approximate Reasoning
Abstract In order to use three-way decision (TWD) to solve multiple attribute decision making (MA... more Abstract In order to use three-way decision (TWD) to solve multiple attribute decision making (MADM) problems, in this article, a new TWD model with intuitionistic fuzzy numbers (IFNs) is proposed. First of all, we define the relative loss functions to demonstrate some features of loss functions in TWDs, which is the basis for future research. Then, based on the correlation between the loss functions and the IFNs, we get the relative loss functions based on IFNs. At the same time, the classification rules of the TWDs are discussed from different viewpoints, including the thresholds and their properties. Aiming at MADM problems with unreasonable values, a new integrated method of relative loss functions is established to obtain a fairer loss integration result of alternatives. In addition, considering that there are no decision attributes and only condition attributes in MADM, we use grey relational degree to calculate the condition probability. In the end, a novel TWD model is proposed to solve MADM problems with IFNs, and a practical example on selecting suppliers is used to demonstrate its effectiveness and practicability.
Question Classification (QC) is of primary importance in question answering systems, since it ena... more Question Classification (QC) is of primary importance in question answering systems, since it enables extraction of the correct answer type. State-of-the-art solutions for short text classification obtained remarkable results by Convolutional Neural Networks (CNNs). However, implementing such models requires choices, usually based on subjective experience, or on rare works comparing different settings for general text classification, while peculiar solutions should be individuated for QC task, depending on language and on dataset size. Therefore, this work aims at suggesting best practices for QC using CNNs. Different datasets were employed: (i) A multilingual set of labelled questions to evaluate the dependence of optimal settings on language; (ii) a large, widely used dataset for validation and comparison. Numerous experiments were executed, to perform a multivariate analysis, for evaluating statistical significance and influence on QC performance of all the factors (regarding tex...
Many nations and world regions have suffered on different aspects and fronts from the novel Covid... more Many nations and world regions have suffered on different aspects and fronts from the novel Covid-19. The Scientific community has done great challenges to tackle this virus and its devastating pandemic behavior. Applied Intelligence was part of such a community and has opened the special issue calls. These special-issue calls have established an avenue disseminating research outcomes that have contributed to understanding and taking a lead on the pandemic by pointing out ways to dominate the virus more than it dominates us. Applied Intelligence has received a big number of submissions in large as 480 articles. The journal has done extensive reviews weighing these submissions to differentiate the best in terms of quality, novelty, and its technical contribution on research and development on novel Covid-19. This special issue has incubated 25 articles gathered in Covid-19 special issue in Applied Intelligence; each has been reviewed by at least three expert reviewers and then selected based on the quality of its contribution on the subject. A few articles Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Intuitionistic fuzzy multiple attribute decision making deals with the issue of ranking alternati... more Intuitionistic fuzzy multiple attribute decision making deals with the issue of ranking alternatives based on the decision information quantified in terms of intuitionistic fuzzy values. Lexicographic orders can serve as efficient and indispensable tools for comparing intuitionistic fuzzy values. This paper introduces a number of lexicographic orders by means of several measures such as the membership, non-membership, score, accuracy and expectation score functions. Some equivalent characterizations and illustrative examples are provided, from which the relationships among these lexicographic orders are ascertained. We also propose three different compatible properties of preorders with respect to the algebraic sum and scalar product operations of intuitionistic fuzzy values, and apply them to the investigation of compatible properties of various lexicographic orders. In addition, a benchmark problem regarding risk investment is further explored to give a comparative analysis of dif...
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