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

Spatial and temporal-based query disambiguation for improving web search

Queries submitted to search engines are ambiguous in nature due to users’ irrelevant input which poses real challenges to web search engines both towards understanding a query and giving results. A lot of irrelevant and ambiguous information creates disappointment among users. Thus, this research proposes an ambiguity evolvement process followed by an integrated use of spatial and temporal features to alleviate the search results imprecision. To enhance the effectiveness of web information retrieval the study develops an enhanced Adaptive Disambiguation Approach for web search queries to overcome the problems caused by ambiguous queries. A query classification method was used to filter search results to overcome the imprecision. An algorithm was utilized for finding the similarity of the search results based on spatial and temporal features. Users’ selection based on web results facilitated recording of implicit feedback which was then utilized for web search improvement. Performance evaluation was conducted on data sets GISQC_DS, AMBIENT and MORESQUE comprising of ambiguous queries to certify the effectiveness of the proposed approach in comparison to a well-known temporal evaluation and two-box search methods. The implemented prototype is focused on ambiguous queries to be classified by spatial or temporal features. Spatial queries focus on targeting the location information whereas temporal queries target time in years. In conclusion, the study used search results in the context of Spatial Information Retrieval (S-IR) along with temporal information. Experiments results show that the use of spatial and temporal features in combination can significantly improve the performance in terms of precision (92%), accuracy (93%), recall (95%), and f-measure (93%). Moreover, the use of implicit feedback has a significant impact on the search results which has been demonstrated through experimental evaluation.SHAHID KAMA

Feature Selection for Document Classification : Case Study of Meta-heuristic Intelligence and Traditional Approaches

Doctor of Philosophy (Computer Engineering), 2020Nowadays, the culture for accessing news around the world is changed from paper to electronic format and the rate of publication for newspapers and magazines on website are increased dramatically. Meanwhile, text feature selection for the automatic document classification (ADC) is becoming a big challenge because of the unstructured nature of text feature, which is called “multi-dimension feature problem”. On the other hand, various powerful schemes dealing with text feature selection are being developed continuously nowadays, but there still exists a research gap for “optimization of feature selection problem (OFSP)”, which can be looked for the global optimal features. Meanwhile, the capacity of meta-heuristic intelligence for knowledge discovery process (KDP) is also become the critical role to overcome NP-hard problem of OFSP by providing effective performance and efficient computation time. Therefore, the idea of meta-heuristic based approach for optimization of feature selection is proposed in this research to search the global optimal features for ADC. In this thesis, case study of meta-heuristic intelligence and traditional approaches for feature selection optimization process in document classification is observed. It includes eleven meta-heuristic algorithms such as Ant Colony search, Artificial Bee Colony search, Bat search, Cuckoo search, Evolutionary search, Elephant search, Firefly search, Flower search, Genetic search, Rhinoceros search, and Wolf search, for searching the optimal feature subset for document classification. Then, the results of proposed model are compared with three traditional search algorithms like Best First search (BFS), Greedy Stepwise (GS), and Ranker search (RS). In addition, the framework of data mining is applied. It involves data preprocessing, feature engineering, building learning model and evaluating the performance of proposed meta-heuristic intelligence-based feature selection using various performance and computation complexity evaluation schemes. In data processing, tokenization, stop-words handling, stemming and lemmatizing, and normalization are applied. In feature engineering process, n-gram TF-IDF feature extraction is used for implementing feature vector and both filter and wrapper approach are applied for observing different cases. In addition, three different classifiers like J48, Naïve Bayes, and Support Vector Machine, are used for building the document classification model. According to the results, the proposed system can reduce the number of selected features dramatically that can deteriorate learning model performance. In addition, the selected global subset features can yield better performance than traditional search according to single objective function of proposed model

An enhanced gated recurrent unit with auto-encoder for solving text classification problems

Classification has become an important task for categorizing documents automatically based on their respective groups. Gated Recurrent Unit (GRU) is a type of Recurrent Neural Networks (RNNs), and a deep learning algorithm that contains update gate and reset gate. It is considered as one of the most efficient text classification techniques, specifically on sequential datasets. However, GRU suffered from three major issues when it is applied for solving the text classification problems. The first drawback is the failure in data dimensionality reduction, which leads to low quality solution for the classification problems. Secondly, GRU still has difficulty in training procedure due to redundancy between update and reset gates. The reset gate creates complexity and require high processing time. Thirdly, GRU also has a problem with informative features loss in each recurrence during the training phase and high computational cost. The reason behind this failure is due to a random selection of features from datasets (or previous outputs), when applied in its standard form. Therefore, in this research, a new model namely Encoder Simplified GRU (ES-GRU) is proposed to reduce dimension of data using an Auto-Encoder (AE). Accordingly, the reset gate is replaced with an update gate in order to reduce the redundancy and complexity in the standard GRU. Finally, a Batch Normalization method is incorporated in the GRU and AE for improving the performance of the proposed ES-GRU model. The proposed model has been evaluated on seven benchmark text datasets and compared with six baselines well-known multiclass text classification approaches included standard GRU, AE, Long Short Term Memory, Convolutional Neural Network, Support Vector Machine, and Naïve Bayes. Based on various types of performance evaluation parameters, a considerable amount of improvement has been observed in the performance of the proposed model as compared to other standard classification techniques, and showed better effectiveness and efficiency of the developed model

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

User Review Analysis for Requirement Elicitation: Thesis and the framework prototype's source code

Online reviews are an important channel for requirement elicitation. However, requirement engineers face challenges when analysing online user reviews, such as data volumes, technical supports, existing techniques, and legal barriers. Juan Wang proposes a framework solving user review analysis problems for the purpose of requirement elicitation that sets up a channel from downloading user reviews to structured analysis data. The main contributions of her work are: (1) the thesis proposed a framework to solve the user review analysis problem for requirement elicitation; (2) the prototype of this framework proves its feasibility; (3) the experiments prove the effectiveness and efficiency of this framework. This resource here is the latest version of Juan Wang's PhD thesis "User Review Analysis for Requirement Elicitation" and all the source code of the prototype for the framework as the results of her thesis

Computer Science & Technology Series : XXI Argentine Congress of Computer Science. Selected papers

CACIC’15 was the 21thCongress in the CACIC series. It was organized by the School of Technology at the UNNOBA (North-West of Buenos Aires National University) in Junín, Buenos Aires. The Congress included 13 Workshops with 131 accepted papers, 4 Conferences, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 6 courses. CACIC 2015 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 202 submissions. An average of 2.5 review reports werecollected for each paper, for a grand total of 495 review reports that involved about 191 different reviewers. A total of 131 full papers, involving 404 authors and 75 Universities, were accepted and 24 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

Computer Science & Technology Series : XXI Argentine Congress of Computer Science. Selected papers

CACIC’15 was the 21thCongress in the CACIC series. It was organized by the School of Technology at the UNNOBA (North-West of Buenos Aires National University) in Junín, Buenos Aires. The Congress included 13 Workshops with 131 accepted papers, 4 Conferences, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 6 courses. CACIC 2015 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 202 submissions. An average of 2.5 review reports werecollected for each paper, for a grand total of 495 review reports that involved about 191 different reviewers. A total of 131 full papers, involving 404 authors and 75 Universities, were accepted and 24 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI