Concept Based Labeling of Text Documents Using Support Vector Machine

Classification plays a vital role in many information management and retrieval tasks . Text classification uses labeled training data to learn the classification system and then automatically classifies the remaining text using the lear ned system. Classification follows various techniques such as text processing, feature extraction, feature vector construction and final classification. The proposed mining model consists of sentence - based concept analysis, document - based concept analysis, corpus - based concept - analysis, and concept - based similarity measure. The proposed model can efficiently find significant matching concepts between documents, according to the semantics of their sentences. The similarity between documents is calculate d bas ed on a n similarity measure. Then we analyze the term that contributes to the sentence semantics on the sentence, document, and corpus levels rather than the traditional analysis of the document only. With the extracted feature vector for each new document, Support Vector Machine (SVM) algorithm is applied for document classification. The approach enhances the text classification accuracy

Learning to Taste: A Multimodal Wine Dataset

We present WineSensed, a large multimodal wine dataset for studying the relations between visual perception, language, and flavor. The dataset encompasses 897k images of wine labels and 824k reviews of wines curated from the Vivino platform. It has over 350k unique vintages, annotated with year, region, rating, alcohol percentage, price, and grape composition. We obtained fine-grained flavor annotations on a subset by conducting a wine-tasting experiment with 256 participants who were asked to rank wines based on their similarity in flavor, resulting in more than 5k pairwise flavor distances. We propose a low-dimensional concept embedding algorithm that combines human experience with automatic machine similarity kernels. We demonstrate that this shared concept embedding space improves upon separate embedding spaces for coarse flavor classification (alcohol percentage, country, grape, price, rating) and aligns with the intricate human perception of flavor.Comment: Accepted to NeurIPS 2023. See project page: https://thoranna.github.io/learning_to_taste

Coupled similarity analysis in supervised learning

University of Technology Sydney. Faculty of Engineering and Information Technology.In supervised learning, the distance or similarity measure is widely used in a lot of classification algorithms. When calculating the categorical data similarity, the strategy used by the traditional classifiers often overlooks the inter-relationship between different data attributes and assumes that they are independent of each other. This can be seen, for example, in the overlap similarity and the frequency based similarity. While for the numerical data, the most used Euclidean distance or Minkowski distance is restricted in each single feature and assumes the features in the dataset have no outer connections. That can cause problems in expressing the real similarity or distance between instances and may give incorrect results if the inter-relationship between attributes is ignored. The same problems exist in other supervised learning, such as the classification tasks of class-imbalance or multi-label. In order to solve these research limitations and challenges, this thesis proposes an insightful analysis on coupled similarity in supervised learning to give an expression of similarity that is more closely related to the real nature of the problem. Firstly, we propose a coupled fuzzy kNN to classify imbalanced categorical data which have strong relationships between objects, attributes and classes in Chapter 3. It incorporates the size membership of a class with attribute weight into a coupled similarity measure, which effectively extracts the intercoupling and intra-coupling relationships from categorical attributes. As it reveals the true inner-relationship between attributes, the similarity strategy we have used can make the instances of each class more compact when measured by the distance. That brings substantial benefits when dealing with class imbalance data. The experiment results show that our supposed method has a more stable and higher average performance than the classic algorithms. We also introduce a coupled similar distance for continuous features, by considering the intra-coupled relationship and inter-coupled relationship between the numerical attributes and their corresponding extensions. As detailed in Chapter 4, we calculate the coupling distance between continuous features based on discrete groups. Substantial experiments have verified that our coupled distance outperforms the original distance, and this is also supported by statistical analysis. When considering the similarity concept, people may only relate to the categorical data, while for the distance concept, people may only take into account the numerical data. Seldom have methods taken into account the both concepts, especially when considering the coupling relationship between features. In Chapter 5, we propose a new method which integrates our coupling concept for mixed type data. In our method, we first do discretization on numerical attributes to transfer such continuous values into separate groups, so as to adopt the inter-coupling distance as we do on categorical features (coupling similarity), then we combine this new coupled distance to the original distance (Euclidean distance), to overcome the shortcoming of the previous algorithms. The experiment results show some improvement when compared to the basic and some variants of kNN algorithms. We also extend our coupling concept to multi-label classification tasks. The traditional single-label classifiers are known to be not suitable for multi-label tasks anymore, owing to the overlap concept of the class labels. The most used classifier in multi-label problems, ML-kNN, learns a single classifier for each label independently, so it is actually a binary relevance classifier. As a consequence, this algorithm is often criticized. To overcome this drawback, we introduce a coupled label similarity, which explores the inner relationship between different labels in multi-label classification according to their natural co-occurrence. This similarity reflects the distance of the different classes. By integrating this similarity with the multi-label kNN algorithm, we improve the performance significantly. Evaluated over three commonly used verification criteria for multi-label classifiers, our proposed coupled multi-label classifier outperforms the ML-kNN, BR-kNN and even IBLR. The result indicates that our supposed coupled label similarity is appropriate for multi-label learning problems and can work more effectively compared to other methods. All the classifiers analyzed in this thesis are based on our coupling similarity (or distance), and applied to different tasks in supervised learning. The performance of these models is examined by widely used verification criteria, such as ROC, Accuracy Rate, Average Precision and Hamming Loss. This thesis provides insightful knowledge for investors to find the inner relationship between features in supervised learning tasks

Multistep Fuzzy Bridged Refinement Domain Adaptation Algorithm and Its Application to Bank Failure Prediction

© 2015 IEEE. Machine learning plays an important role in data classification and data-based prediction. In some real-world applications, however, the training data (coming from the source domain) and test data (from the target domain) come from different domains or time periods, and this may result in the different distributions of some features. Moreover, the values of the features and/or labels of the datasets might be nonnumeric and involve vague values. Traditional learning-based prediction and classification methods cannot handle these two issues. In this study, we propose a multistep fuzzy bridged refinement domain adaptation algorithm, which offers an effective way to deal with both issues. It utilizes a concept of similarity to modify the labels of the target instances that were initially predicted by a shift-unaware model. It then refines the labels using instances that are most similar to a given target instance. These instances are extracted from mixture domains composed of source and target domains. The proposed algorithm is built on a basis of some data and refines the labels, thus performing completely independently of the shift-unaware prediction model. The algorithm uses a fuzzy set-based approach to deal with the vague values of the features and labels. Four different datasets are used in the experiments to validate the proposed algorithm. The results, which are compared with those generated by the existing domain adaptation methods, demonstrate a significant improvement in prediction accuracy in both the above-mentioned datasets

Incremental learning of independent, overlapping, and graded concept descriptions with an instance-based process framework

Supervised learning algorithms make several simplifying assumptions concerning the characteristics of the concept descriptions to be learned. For example, concepts are often assumed to be (1) defined with respect to the same set of relevant attributes, (2) disjoint in instance space, and (3) have uniform instance distributions. While these assumptions constrain the learning task, they unfortunately limit an algorithm's applicability. We believe that supervised learning algorithms should learn attribute relevancies independently for each concept, allow instances to be members of any subset of concepts, and represent graded concept descriptions. This paper introduces a process framework for instance-based learning algorithms that exploit only specific instance and performance feedback information to guide their concept learning processes. We also introduce Bloom, a specific instantiation of this framework. Bloom is a supervised, incremental, instance-based learning algorithm that learns relative attribute relevancies independently for each concept, allows instances to be members of any subset of concepts, and represents graded concept memberships. We describe empirical evidence to support our claims that Bloom can learn independent, overlapping, and graded concept descriptions

Improving ICD-based semantic similarity by accounting for varying degrees of comorbidity

Finding similar patients is a common objective in precision medicine, facilitating treatment outcome assessment and clinical decision support. Choosing widely-available patient features and appropriate mathematical methods for similarity calculations is crucial. International Statistical Classification of Diseases and Related Health Problems (ICD) codes are used worldwide to encode diseases and are available for nearly all patients. Aggregated as sets consisting of primary and secondary diagnoses they can display a degree of comorbidity and reveal comorbidity patterns. It is possible to compute the similarity of patients based on their ICD codes by using semantic similarity algorithms. These algorithms have been traditionally evaluated using a single-term expert rated data set. However, real-word patient data often display varying degrees of documented comorbidities that might impair algorithm performance. To account for this, we present a scale term that considers documented comorbidity-variance. In this work, we compared the performance of 80 combinations of established algorithms in terms of semantic similarity based on ICD-code sets. The sets have been extracted from patients with a C25.X (pancreatic cancer) primary diagnosis and provide a variety of different combinations of ICD-codes. Using our scale term we yielded the best results with a combination of level-based information content, Leacock & Chodorow concept similarity and bipartite graph matching for the set similarities reaching a correlation of 0.75 with our expert's ground truth. Our results highlight the importance of accounting for comorbidity variance while demonstrating how well current semantic similarity algorithms perform.Comment: 11 pages, 6 figures, 1 tabl

Detecting and removing noisy instances from concept descriptions

Several published results show that instance-based learning algorithms record high classification accuracies and low storage requirements when applied to supervised learning tasks. However, these learning algorithms are highly sensitive to training set noise. This paper describes a simple extension of instance-based learning algorithms for detecting and removing noisy instances from concept descriptions. The extension requires evidence that saved instances be significantly good classifiers before it allows them to be used for subsequent classification tasks. We show that this extension's performance degrades more slowly in the presence of noise, improves classification accuracies, and further reduces storage requirements in several artificial and real-world databases