GAMoN: Discovering M-of-N{¬,∨} hypotheses for text classification by a lattice-based Genetic Algorithm

AbstractWhile there has been a long history of rule-based text classifiers, to the best of our knowledge no M-of-N-based approach for text categorization has so far been proposed. In this paper we argue that M-of-N hypotheses are particularly suitable to model the text classification task because of the so-called “family resemblance” metaphor: “the members (i.e., documents) of a family (i.e., category) share some small number of features, yet there is no common feature among all of them. Nevertheless, they resemble each other”. Starting from this conjecture, we provide a sound extension of the M-of-N approach with negation and disjunction, called M-of-N{¬,∨}, which enables to best fit the true structure of the data. Based on a thorough theoretical study, we show that the M-of-N{¬,∨} hypothesis space has two partial orders that form complete lattices.GAMoN is the task-specific Genetic Algorithm (GA) which, by exploiting the lattice-based structure of the hypothesis space, efficiently induces accurate M-of-N{¬,∨} hypotheses.Benchmarking was performed over 13 real-world text data sets, by using four rule induction algorithms: two GAs, namely, BioHEL and OlexGA, and two non-evolutionary algorithms, namely, C4.5 and Ripper. Further, we included in our study linear SVM, as it is reported to be among the best methods for text categorization. Experimental results demonstrate that GAMoN delivers state-of-the-art classification performance, providing a good balance between accuracy and model complexity. Further, they show that GAMoN can scale up to large and realistic real-world domains better than both C4.5 and Ripper

Machine learning algorithms distinguish discrete digital emotional fingerprints for web pages related to back pain

Back pain is the leading cause of disability worldwide. Its emergence relates not only to the musculoskeletal degeneration biological substrate but also to psychosocial factors; emotional components play a pivotal role. In modern society, people are significantly informed by the Internet; in turn, they contribute social validation to a “successful” digital information subset in a dynamic interplay. The Affective component of medical pages has not been previously investigated, a significant gap in knowledge since they represent a critical biopsychosocial feature. We tested the hypothesis that successful pages related to spine pathology embed a consistent emotional pattern, allowing discrimination from a control group. The pool of web pages related to spine or hip/knee pathology was automatically selected by relevance and popularity and submitted to automated sentiment analysis to generate emotional patterns. Machine Learning (ML) algorithms were trained to predict page original topics from patterns with binary classification. ML showed high discrimination accuracy; disgust emerged as a discriminating emotion. The findings suggest that the digital affective “successful content” (collective consciousness) integrates patients’ biopsychosocial ecosystem, with potential implications for the emergence of chronic pain, and the endorsement of health-relevant specific behaviors. Awareness of such effects raises practical and ethical issues for health information providers

Supervised Classification Using Balanced Training

We examine supervised learning for multi-class, multi-label text classification. We are interested in exploring classification in a real-world setting, where the distribution of labels may change dynamically over time. First, we compare the performance of an array of binary classifiers trained on the label distribution found in the original corpus against classifiers trained on balanced data, where we try to make the label distribution as nearly uniform as possible. We discuss the performance trade-offs between balanced vs. unbalanced training, and highlight the advantages of balancing the training set. Second, we compare the performance of two classifiers, Naive Bayes and SVM, with several feature-selection methods, using balanced training. We combine a Named-Entity-based rote classifier with the statistical classifiers to obtain better performance than either method alone.Peer reviewe

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

Large-scale Multi-Label Text Classification for an Online News Monitoring System

This thesis provides a detailed exploration of numerous methods — some established and some novel — considered in the construction of a text-categorization system, for use in a large-scale, online news-monitoring system known as PULS. PULS is an information extraction (IE) system, consisting of a number of tools for automatically collecting named-entities from text. The system also has access to large training corpora in the business domain, where documents are annotated with associated industry-sectors. These assets are leveraged in the construction of a multi-label industry-sector classifier, the output of which is displayed on the web-based front-end of PULS, for new articles. Through review of background literature and direct experimentation with each stage of development, we illuminate many major challenges of multi-label classification. These challenges include: working effectively in a real-world scenario that poses time and memory restrictions; organizing and processing semi-structured, pre-annotated text corpora; handling large-scale data sets and label sets with significant class imbalances; weighing the trade-offs of different learning algorithms and feature-selection methods with respect to end-user performance; and finding meaningful evaluations for each system component. In addition to presenting the challenges associated with large-scale multi-label learning, this thesis presents a number of experiments and evaluations to determine methods which enhance overall performance. The major outcome of these experiments is a multi-stage, multi-label classifier that combines IE-based rote classification — with features extracted by the PULS system — with an array of balanced, statistical classifiers. Evaluation of this multi-stage system shows improvement over a baseline classifier and, for certain evaluations, over state-of-the-art performance from literature, when tested on a commonly-used corpus. Aspects of the classification method and their associated experimental results have also been published for international conference proceedings

EFFECTIVE METHODS AND TOOLS FOR MINING APP STORE REVIEWS

Research on mining user reviews in mobile application (app) stores has noticeably advanced in the past few years. The main objective is to extract useful information that app developers can use to build more sustainable apps. In general, existing research on app store mining can be classified into three genres: classification of user feedback into different types of software maintenance requests (e.g., bug reports and feature requests), building practical tools that are readily available for developers to use, and proposing visions for enhanced mobile app stores that integrate multiple sources of user feedback to ensure app survivability. Despite these major advances, existing tools and techniques still suffer from several drawbacks. Specifically, the majority of techniques rely on the textual content of user reviews for classification. However, due to the inherently diverse and unstructured nature of user-generated online textual reviews, text-based review mining techniques often produce excessively complicated models that are prone to over-fitting. Furthermore, the majority of proposed techniques focus on extracting and classifying the functional requirements in mobile app reviews, providing a little or no support for extracting and synthesizing the non-functional requirements (NFRs) raised in user feedback (e.g., security, reliability, and usability). In terms of tool support, existing tools are still far from being adequate for practical applications. In general, there is a lack of off-the-shelf tools that can be used by researchers and practitioners to accurately mine user reviews. Motivated by these observations, in this dissertation, we explore several research directions aimed at addressing the current issues and shortcomings in app store review mining research. In particular, we introduce a novel semantically aware approach for mining and classifying functional requirements from app store reviews. This approach reduces the dimensionality of the data and enhances the predictive capabilities of the classifier. We then present a two-phase study aimed at automatically capturing the NFRs in user reviews. We also introduce MARC, a tool that enables developers to extract, classify, and summarize user reviews

Towards Fault Diagnosis in Reconfigurable Robot

東京電機大学201

Memetic micro-genetic algorithms for cancer data classification

Fast and precise medical diagnosis of human cancer is crucial for treatment decisions. Gene selection consists of identifying a set of informative genes from microarray data to allow high predictive accuracy in human cancer classification. This task is a combinatorial search problem, and optimisation methods can be applied for its resolution. In this paper, two memetic micro-genetic algorithms (MμV1 and MμV2) with different hybridisation approaches are proposed for feature selection of cancer microarray data. Seven gene expression datasets are used for experimentation. The comparison with stochastic state-of-the-art optimisation techniques concludes that problem-dependent local search methods combined with micro-genetic algorithms improve feature selection of cancer microarray data.Fil: Rojas, Matias Gabriel. Universidad Nacional de Lujan. Centro de Investigacion Docencia y Extension En Tecnologias de la Informacion y Las Comunicaciones.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Olivera, Ana Carolina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina. Universidad Nacional de Lujan. Centro de Investigacion Docencia y Extension En Tecnologias de la Informacion y Las Comunicaciones.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Vidal, Pablo Javier. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentin