Polychotomiser for case-based reasoning beyond the traditional Bayesian classification approach

This work implements an enhanced Bayesian classifier with better performance as compared to the ordinary naïve Bayes classifier when used with domains and datasets of varying characteristics. Text classification is an active and on-going research field of Artificial Intelligence (AI). Text classification is defined as the task of learning methods for categorising collections of electronic text documents into their annotated classes, based on its contents. An increasing number of statistical approaches have been developed for text classification, including k-nearest neighbor classification, naïve Bayes classification, decision tree, rules induction, and the algorithm implementing the structural risk minimisation theory called the support vector machine. Among the approaches used in these applications, naïve Bayes classifiers have been widely used because of its simplicity. However this generative method has been reported to be less accurate than the discriminative methods such as SVM. Some researches have proven that the naïve Bayes classifier performs surprisingly well in many other domains with certain specialised characteristics. The main aim of this work is to quantify the weakness of traditional naïve Bayes classification and introduce an enhance Bayesian classification approach with additional innovative techniques to perform better than the traditional naïve Bayes classifier. Our research goal is to develop an enhanced Bayesian probabilistic classifier by introducing different tournament structures ranking algorithms along with a high relevance keywords extraction facility and an accurately calculated weighting factors facility. These were done to improve the performance of the classification tasks for specific datasets with different characteristics. Other researches have used general datasets, such as Reuters-21578 and 20_newsgroups to validate the performance of their classifiers. Our approach is easily adapted to datasets with different characteristics in terms of the degree of similarity between classes, multi-categorised documents, and different dataset organisations. As previously mentioned we introduce several techniques such as tournament structures ranking algorithms, higher relevance keyword extraction, and automatically computed document dependent (ACDD) weighting factors. Each technique has unique response while been implemented in datasets with different characteristics but has shown to give outstanding performance in most cases. We have successfully optimised our techniques for individual datasets with different characteristics based on our experimental results

Challenges in Short Text Classification: The Case of Online Auction Disclosure

Text classification is an important research problem in many fields. We examine a special case of textual content namely, short text. Examples of short text appear in a number of contexts such as online reviews, chat messages, twitter feeds, etc. In this research, we examine short text for the purpose of classification in internet auctions. The “ask seller a question” forum of a large horizontal intermediary auction platform is used to conduct this research. We describe our approach to classification by examining various solution methods to the problem. The unsupervised K-Medoids clustering algorithm provides useful but limited insights into keywords extraction while the supervised Naïve Bayes algorithm successfully achieves on average, around 65% classification accuracy. We then present a score assigning approach to this issue which outperforms the other two methods. Finally, we discuss how our approach to short text classification can be used to analyse the effectiveness of internet auctions

Knowledge Discovery and Management within Service Centers

These days, most enterprise service centers deploy Knowledge Discovery and Management (KDM) systems to address the challenge of timely delivery of a resourceful service request resolution while efficiently utilizing the huge amount of data. These KDM systems facilitate prompt response to the critical service requests and if possible then try to prevent the service requests getting triggered in the first place. Nevertheless, in most cases, information required for a request resolution is dispersed and suppressed under the mountain of irrelevant information over the Internet in unstructured and heterogeneous formats. These heterogeneous data sources and formats complicate the access to reusable knowledge and increase the response time required to reach a resolution. Moreover, the state-of-the art methods neither support effective integration of domain knowledge with the KDM systems nor promote the assimilation of reusable knowledge or Intellectual Capital (IC). With the goal of providing an improved service request resolution within the shortest possible time, this research proposes an IC Management System. The proposed tool efficiently utilizes domain knowledge in the form of semantic web technology to extract the most valuable information from those raw unstructured data and uses that knowledge to formulate service resolution model as a combination of efficient data search, classification, clustering, and recommendation methods. Our proposed solution also handles the technology categorization of a service request which is very crucial in the request resolution process. The system has been extensively evaluated with several experiments and has been used in a real enterprise customer service center

Mining of textual databases within the product development process

Ph.DNUS-TU/E JOINT PH.D. PROGRAMM

Multi modal multi-semantic image retrieval

PhDThe rapid growth in the volume of visual information, e.g. image, and video can overwhelm users’ ability to find and access the specific visual information of interest to them. In recent years, ontology knowledge-based (KB) image information retrieval techniques have been adopted into in order to attempt to extract knowledge from these images, enhancing the retrieval performance. A KB framework is presented to promote semi-automatic annotation and semantic image retrieval using multimodal cues (visual features and text captions). In addition, a hierarchical structure for the KB allows metadata to be shared that supports multi-semantics (polysemy) for concepts. The framework builds up an effective knowledge base pertaining to a domain specific image collection, e.g. sports, and is able to disambiguate and assign high level semantics to ‘unannotated’ images. Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’ model (BVW) as an effective method to represent visual content information and to enhance its classification and retrieval. Local features are more useful than global features, e.g. colour, shape or texture, as they are invariant to image scale, orientation and camera angle. An innovative approach is proposed for the representation, annotation and retrieval of visual content using a hybrid technique based upon the use of an unstructured visual word and upon a (structured) hierarchical ontology KB model. The structural model facilitates the disambiguation of unstructured visual words and a more effective classification of visual content, compared to a vector space model, through exploiting local conceptual structures and their relationships. The key contributions of this framework in using local features for image representation include: first, a method to generate visual words using the semantic local adaptive clustering (SLAC) algorithm which takes term weight and spatial locations of keypoints into account. Consequently, the semantic information is preserved. Second a technique is used to detect the domain specific ‘non-informative visual words’ which are ineffective at representing the content of visual data and degrade its categorisation ability. Third, a method to combine an ontology model with xi a visual word model to resolve synonym (visual heterogeneity) and polysemy problems, is proposed. The experimental results show that this approach can discover semantically meaningful visual content descriptions and recognise specific events, e.g., sports events, depicted in images efficiently. Since discovering the semantics of an image is an extremely challenging problem, one promising approach to enhance visual content interpretation is to use any associated textual information that accompanies an image, as a cue to predict the meaning of an image, by transforming this textual information into a structured annotation for an image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct types of information representation and modality, there are some strong, invariant, implicit, connections between images and any accompanying text information. Semantic analysis of image captions can be used by image retrieval systems to retrieve selected images more precisely. To do this, a Natural Language Processing (NLP) is exploited firstly in order to extract concepts from image captions. Next, an ontology-based knowledge model is deployed in order to resolve natural language ambiguities. To deal with the accompanying text information, two methods to extract knowledge from textual information have been proposed. First, metadata can be extracted automatically from text captions and restructured with respect to a semantic model. Second, the use of LSI in relation to a domain-specific ontology-based knowledge model enables the combined framework to tolerate ambiguities and variations (incompleteness) of metadata. The use of the ontology-based knowledge model allows the system to find indirectly relevant concepts in image captions and thus leverage these to represent the semantics of images at a higher level. Experimental results show that the proposed framework significantly enhances image retrieval and leads to narrowing of the semantic gap between lower level machinederived and higher level human-understandable conceptualisation

Penerapan Naive Bayes untuk Mengurangi Data Noise pada Klasifikasi Multi Kelas dengan Decision Tree

Selama beberapa dekade terakhir, cukup banyak algoritma data mining yang telah diusulkan oleh peneliti kecerdasan komputasi untuk memecahkan masalah klasifikasi di dunia nyata. Di antara metode-metode data mining lainnya, Decision Tree (DT) memiliki berbagai keunggulan diantaranya sederhana untuk dipahami, mudah untuk diterapkan, membutuhkan sedikit pengetahuan, mampu menangani data numerik dan kategorikal, tangguh, dan dapat menangani dataset yang besar. Banyak dataset berukuran besar dan memiliki banyak kelas atau multi kelas yang ada di dunia memiliki noise atau mengandung error. Algoritma pengklasifikasi DT memiliki keunggulan dalam menyelesaikan masalah klasifikasi, namun data noise yang terdapat pada dataset berukuran besar dan memiliki banyak kelas atau multi kelas dapat mengurangi akurasi pada klasifikasinya. Masalah data noise pada dataset tersebut akan diselesaikan dengan menerapkan pengklasifikasi Naive Bayes (NB) untuk menemukan instance yang mengandung noise dan menghapusnya sebelum diproses oleh pengklasifikasi DT. Pengujian metode yang diusulkan dilakukan dengan delapan dataset uji dari UCI (University of California, Irvine) machine learning repository dan dibandingkan dengan algoritma pengklasifikasi DT. Hasil akurasi yang didapat menunjukkan bahwa algoritma yang diusulkan DT+NB lebih unggul dari algoritma DT, dengan nilai akurasi untuk masing-masing dataset uji seperti Breast Cancer 96.59% (meningkat 21,06%), Diabetes 92,32% (meningkat 18,49%), Glass 87,50% (meningkat 20,68%), Iris 97,22% (meningkat 1,22%), Soybean 95,28% (meningkat 3,77%), Vote 98,98% (meningkat 2,66%), Image Segmentation 99,10% (meningkat 3,36%), dan Tic-tac-toe 93,85% (meningkat 9,30%). Dengan demikian dapat disimpulkan bahwa penerapan NB terbukti dapat menangani data noise pada dataset berukuran besar dan memiliki banyak kelas atau multi kelas sehingga akurasi pada algoritma klasifikasi DT meningkat

Text Analytics to Predict Time and Cause of Death from Verbal Autopsies

This thesis describes the first Text Analytics approach to predicting Causes of Death (CoD) from Verbal Autopsies (VA). VA is an alternative technique recommended by the World Health Organisation for ascertaining CoD in low and middle-income countries (LMIC). CoD information is vitally important in the provision of healthcare. CoD information from VA can be obtained via two main approaches: manual, also referred to as the physician-review and automatic. The automatic-based approach is an active research area due to its efficiency and cost effectiveness over the manual approach. VA contains both closed responses and open narrative text. However, the open narrative text has been ignored by the state-of-art automatic approaches and this remains a challenge and an important research issue. We hypothesise that it is feasible to predict CoD from the narratives of VA. We further contend that an automatic approach that could utilise the information contained in both narrative and closed response text of VA could lead to an improved prediction accuracy of CoD. This research has been formulated as a Text Classification problem, which employs Corpus and Computational Linguistics, Natural Language Processing and Machine Learning techniques to automatically classify VA documents according to CoD. Firstly, the research uses a VA corpus built from a sample collection of over 11,400 VA documents collected during a 10 year period in Ghana, West Africa. About 80 per cent of these documents have been annotated with CoD by medical experts. Secondly, we design experiments to identify Machine Learning techniques (algorithm, feature representation scheme, and feature reduction strategy) suitable for classifying VA open narratives (VAModel1). Thirdly, we propose novel methods of extracting features to build a model that predicts CoD from VA narratives using the annotated VA corpus as training and testing set. Furthermore, we develop two additional models: only closed responses based (VAModel2); and a hybrid of closed and open narrative based model (VAModel3). Our VAModel1 performs reasonably better than our baseline model, suggesting the feasibility of predicting the CoD from the VA open narratives. Overall, VAModel3 performance was observed to achieve better performance than VAModel1 but not significantly better than VAModel2. Also, in terms of reliability, VAModel1 obtained a moderate agreement (kappa score = 0.4) when compared with the gold standard– medical experts (average annotation agreement between medical experts, kappa score= 0.64). Furthermore, an acceptable agreement was obtained for VAModel2 (kappa score =0.71) and VAModel3 (kappa score =0.75), suggesting the reliability of these two models is better than medical experts. Also, a detailed analysis suggested that combining information from narratives and closed responses leads to an increase in performance for some CoD categories whereas information obtained from the closed responses part is enough for other CoD categories. Our research provides an alternative automatic approach to predicting CoD from VA, which is essential for LMIC. Therefore, further research into various aspects of the modelling process could improve the current performance of automatically predicting CoD from VAs

Optimisation Method for Training Deep Neural Networks in Classification of Non- functional Requirements

Non-functional requirements (NFRs) are regarded critical to a software system's success. The majority of NFR detection and classification solutions have relied on supervised machine learning models. It is hindered by the lack of labelled data for training and necessitate a significant amount of time spent on feature engineering. In this work we explore emerging deep learning techniques to reduce the burden of feature engineering. The goal of this study is to develop an autonomous system that can classify NFRs into multiple classes based on a labelled corpus. In the first section of the thesis, we standardise the NFRs ontology and annotations to produce a corpus based on five attributes: usability, reliability, efficiency, maintainability, and portability. In the second section, the design and implementation of four neural networks, including the artificial neural network, convolutional neural network, long short-term memory, and gated recurrent unit are examined to classify NFRs. These models, necessitate a large corpus. To overcome this limitation, we proposed a new paradigm for data augmentation. This method uses a sort and concatenates strategy to combine two phrases from the same class, resulting in a two-fold increase in data size while keeping the domain vocabulary intact. We compared our method to a baseline (no augmentation) and an existing approach Easy data augmentation (EDA) with pre-trained word embeddings. All training has been performed under two modifications to the data; augmentation on the entire data before train/validation split vs augmentation on train set only. Our findings show that as compared to EDA and baseline, NFRs classification model improved greatly, and CNN outperformed when trained using our suggested technique in the first setting. However, we saw a slight boost in the second experimental setup with just train set augmentation. As a result, we can determine that augmentation of the validation is required in order to achieve acceptable results with our proposed approach. We hope that our ideas will inspire new data augmentation techniques, whether they are generic or task specific. Furthermore, it would also be useful to implement this strategy in other languages