Knowledge Discovery Tasks Research Articles

Dimension Reduction With Prior Information for Knowledge Discovery.

This paper addresses the problem of mapping high-dimensional data to a low-dimensional space, in the presence of other known features. This problem is ubiquitous in science and engineering as there are often controllable/measurable features in most applications. To solve this problem, this paper proposes a broad class of methods, which is referred to as conditional multidimensional scaling (MDS). An algorithm for optimizing the objective function of conditional MDS is also developed. The convergence of this algorithm is proven under mild assumptions. Conditional MDS is illustrated with kinship terms, facial expressions, textile fabrics, car-brand perception, and cylinder machining examples. These examples demonstrate the advantages of conditional MDS over conventional dimension reduction in improving the estimation quality of the reduced-dimension space and simplifying visualization and knowledge discovery tasks. Computer codes for this work are available in the open-source cml R package.

Non-Axiomatic Logic Modeling of English Texts for Knowledge Discovery and Commonsense Reasoning

Non-axiomatic logic (NAL) is a term-based, non-monotonic, multi-valued logic with evidence-based formal semantics. All those characteristics position NAL as an excellent candidate for modeling natural language expressions and supporting artificial agents while performing knowledge discovery and commonsense reasoning tasks. In this article, we propose a set of rules for the automatic translation of natural language (NL) text into the formal language of non-axiomatic logic (NAL). Several free available tools are used to support a previous linguistic analysis, and a common sense ontology is used to populate a background knowledge base that helps to delimit the scope and the semantics of logical formulas translated. Experimentation shows our set to be the most comprehensive NL-to-NAL translation rule set known so far. Furthermore, we included an extensive set of examples to show how our proposed set of rules can be used for translating a wide range of English statements with varying grammatical structures.

Cost-Sensitive Models to Predict Risk of Cardiovascular Events in Patients with Chronic Heart Failure

Chronic heart failure (CHF) is a clinical syndrome characterised by symptoms and signs due to structural and/or functional abnormalities of the heart. CHF confers risk for cardiovascular deterioration events which cause recurrent hospitalisations and high mortality rates. The early prediction of these events is very important to limit serious consequences, improve the quality of care, and reduce its burden. CHF is a progressive condition in which patients may remain asymptomatic before the onset of symptoms, as observed in heart failure with a preserved ejection fraction. The early detection of underlying causes is critical for treatment optimisation and prognosis improvement. To develop models to predict cardiovascular deterioration events in patients with chronic heart failure, a real dataset was constructed and a knowledge discovery task was implemented in this study. The dataset is imbalanced, as it is common in real-world applications. It thus posed a challenge because imbalanced datasets tend to be overwhelmed by the abundance of majority-class instances during the learning process. To address the issue, a pipeline was developed specifically to handle imbalanced data. Different predictive models were developed and compared. To enhance sensitivity and other performance metrics, we employed multiple approaches, including data resampling, cost-sensitive methods, and a hybrid method that combines both techniques. These methods were utilised to assess the predictive capabilities of the models and their effectiveness in handling imbalanced data. By using these metrics, we aimed to identify the most effective strategies for achieving improved model performance in real scenarios with imbalanced datasets. The best model for predicting cardiovascular events achieved mean a sensitivity 65%, a mean specificity 55%, and a mean area under the curve of 0.71. The results show that cost-sensitive models combined with over/under sampling approaches are effective for the meaningful prediction of cardiovascular events in CHF patients.

High-order interaction feature selection for classification learning: A robust knowledge metric perspective

Feature selection is an important learning task in data mining and knowledge discovery. Nevertheless, the fuzziness, uncertainty, and noise presented by the data greatly complicate the construction of learning models. Moreover, most works focus on exploring low-order correlations between variables using low-dimensional mutual information, without paying attention to high-order interaction for multiple variables, resulting in the loss of some potentially important dependency information. Driven by these two issues, a robust knowledge metric approach is invented to perceive and excavate the latent information hidden in interaction. In this study, firstly, a robust fuzzy granularity space is constructed from different granular structures induced by different features, and the robust fuzzy uncertainty measures (RFUMs) are successively devised. Then, RFUMs are used to measure pair-wise, three-order, and even higher-order interaction dependencies among features. Further, a constrained high-order interaction evaluation function inspired by the N-gram language model is formulated, and a corresponding high-order interaction feature selection algorithm with RFUMs (HIFS-RFUMs) is designed. Next, comparative experiments with seven representative algorithms on twenty datasets illustrate its effectiveness. In addition, ablation experiments are conducted on the high-order interaction feature selection algorithm with fuzzy uncertainty measures (HIFS-FUMs) and the relative reduction algorithm with RFUMs (R2-RFUMs), which demonstrate the robustness of the metric and the effectiveness for mining high-order interactive features, respectively.

Unsupervised discretization by two-dimensional MDL-based histogram

Unsupervised discretization is a crucial step in many knowledge discovery tasks. The state-of-the-art method for one-dimensional data infers locally adaptive histograms using the minimum description length (MDL) principle, but the multi-dimensional case is far less studied: current methods consider the dimensions one at a time (if not independently), which result in discretizations based on rectangular cells of adaptive size. Unfortunately, this approach is unable to adequately characterize dependencies among dimensions and/or results in discretizations consisting of more cells (or bins) than is desirable. To address this problem, we propose an expressive model class that allows for far more flexible partitions of two-dimensional data. We extend the state of the art for the one-dimensional case to obtain a model selection problem based on the normalized maximum likelihood, a form of refined MDL. As the flexibility of our model class comes at the cost of a vast search space, we introduce a heuristic algorithm, named PALM, which partitions each dimension alternately and then merges neighboring regions, all using the MDL principle. Experiments on synthetic data show that PALM (1) accurately reveals ground truth partitions that are within the model class (i.e., the search space), given a large enough sample size; (2) approximates well a wide range of partitions outside the model class; (3) converges, in contrast to the state-of-the-art multivariate discretization method IPD. Finally, we apply our algorithm to three spatial datasets, and we demonstrate that, compared to kernel density estimation (KDE), our algorithm not only reveals more detailed density changes, but also fits unseen data better, as measured by the log-likelihood.

Distributed Feature Selection Considering Data Pricing Based on Edge Computing in Electricity Spot Markets

With the rapid development of information technology, the multisource heterogeneous data containing meaningful information have been significantly generated by various edge devices in Internet of Energy, which is one of essential foundations of many knowledge discovery tasks based on edge computing. For some complicated tasks, essential features are owned by different data sellers offering data by blockchains. With limited budgets, buying features are crucial steps in knowledge discovery tasks in electricity spot markets, especially for learning-based algorithms. However, there are lack of proper data pricing mechanisms tailored to dynamic learning processes. Besides, existing methods cannot efficiently employ edge computing servers to obtain optimal policies for selecting features according to dynamic pricing with limited budgets. To overcome such drawbacks, a data pricing mechanism is proposed in this article, which consists of static and dynamic pricing parts. Based on this mechanism, given limited budgets, a feature selection (FS) algorithm considering multiple new factors is proposed, which offers near-optimal solutions for FS at different scenarios. Numeric results show the effectiveness of the proposed algorithms.

Single-valued Plithogenic entropy measurement

In the last decade many researchers paid attention towards uncertainty measurement in given attributes arises unconsciously. These types of uncertainty measurement become more complex in case of dynamic changes in multi-variable, multi-attributes or Plithogenic attributes. It creates major problem at time of knowledge discovery and processing tasks. To resolve this issue, a method is proposed in this paper for precise measurement of randomness arises unconsciously for single valued Plithogenic attribute. In addition the selection of some important Plithogenic attributes at user defined granulation is discussed with an illustrative example.

Random Maximum 2 Satisfiability Logic in Discrete Hopfield Neural Network Incorporating Improved Election Algorithm

Real life logical rule is not always satisfiable in nature due to the redundant variable that represents the logical formulation. Thus, the intelligence system must be optimally governed to ensure the system can behave according to non-satisfiable structure that finds practical applications particularly in knowledge discovery tasks. In this paper, we a propose non-satisfiability logical rule that combines two sub-logical rules, namely Maximum 2 Satisfiability and Random 2 Satisfiability, that play a vital role in creating explainable artificial intelligence. Interestingly, the combination will result in the negative logical outcome where the cost function of the proposed logic is always more than zero. The proposed logical rule is implemented into Discrete Hopfield Neural Network by computing the cost function associated with each variable in Random 2 Satisfiability. Since the proposed logical rule is difficult to be optimized during training phase of DHNN, Election Algorithm is implemented to find consistent interpretation that minimizes the cost function of the proposed logical rule. Election Algorithm has become the most popular optimization metaheuristic technique for resolving constraint optimization problems. The fundamental concepts of Election Algorithm are taken from socio-political phenomena which use new and efficient processes to produce the best outcome. The behavior of Random Maximum 2 Satisfiability in Discrete Hopfield Neural Network is investigated based on several performance metrics. The performance is compared between existing conventional methods with Genetic Algorithm and Election Algorithm. The results demonstrate that the proposed Random Maximum 2 Satisfiability can become the symbolic instruction in Discrete Hopfield Neural Network where Election Algorithm has performed as an effective training process of Discrete Hopfield Neural Network compared to Genetic Algorithm and Exhaustive Search.

Towards Machine Learning Algorithms in Predicting the Clinical Evolution of Patients Diagnosed with COVID-19

Predictive modelling strategies can optimise the clinical diagnostic process by identifying patterns among various symptoms and risk factors, such as those presented in cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus (COVID-19). In this context, the present research proposes a comparative analysis using benchmarking techniques to evaluate and validate the performance of some classification algorithms applied to the same dataset, which contains information collected from patients diagnosed with COVID-19, registered in the Influenza Epidemiological Surveillance System (SIVEP). With this approach, 30,000 cases were analysed during the training and testing phase of the prediction models. This work proposes a comparative approach of machine learning algorithms (ML), working on the knowledge discovery task to predict clinical evolution in patients diagnosed with COVID-19. Our experiments show, through appropriate metrics, that the clinical evolution classification process of patients diagnosed with COVID-19 using the Multilayer Perceptron algorithm performs well against other ML algorithms. Its use has significant consequences for vital prognosis and agility in measures used in the first consultations in hospitals.

Science gateway adoption using plug‐in middleware for evidence‐based healthcare data management

SummaryThere is a growing need for next‐generation science gateways to increase the accessibility of emerging large‐scale datasets for data consumers (e.g., clinicians, researchers) who aim to combat COVID‐19‐related challenges. Such science gateways that enable access to distributed computing resources for large‐scale data management need to be made more programmable, extensible, and scalable. In this article, we propose a novel socio‐technical approach for developing a next‐generation healthcare science gateway, namely, OnTimeEvidence that addresses data consumer challenges surrounding the COVID‐19 pandemic related data analytics. OnTimeEvidence implements an intelligent agent, namely, Vidura Advisor that integrates an evidence‐based filtering method to transform manual practices and improve scalability of data analytics. It also features a plug‐in management middleware that improves the programmability and extensibility of the science gateway capabilities using microservices. Lastly, we present a usability study that shows the important factors from data consumers' perspective to adopt OnTimeEvidence with chatbot‐assisted middleware support to increase their productivity and collaborations to access vast publication archives for rapid knowledge discovery tasks.

Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs.

Epilepsy is a common neurological disorder worldwide and antiepileptic drug (AED) therapy is the cornerstone of its treatment. It has a laudable aim of achieving seizure freedom with minimal, if any, adverse drug reactions (ADRs). Too often, AED treatment is a long-lasting journey, in which ADRs have a crucial role in its administration. Therefore, from a pharmacovigilance perspective, detecting the ADRs of AEDs is a task of utmost importance. Typically, this task is accomplished by analyzing relevant data from spontaneous reporting systems. Despite their wide adoption for pharmacovigilance activities, the passiveness and high underreporting ratio associated with spontaneous reporting systems have encouraged the consideration of other data sources such as electronic health databases and pharmaceutical databases. Social media is the most recent alternative data source with many promising potentials to overcome the shortcomings of traditional data sources. Although in the literature some attempts have investigated the validity and utility of social media for ADR detection of different groups of drugs, none of them was dedicated to the ADRs of AEDs. Hence, this paper presents a novel investigation of the validity and utility of social media as an alternative data source for the detection of AED ADRs. To this end, a dataset of consumer reviews from two online health communities has been collected. The dataset is preprocessed; the unigram, bigram, and trigram are generated; and the ADRs of each AED are extracted with the aid of consumer health vocabulary and ADR lexicon. Three widely used measures, namely, proportional reporting ratio, reporting odds ratio, and information component, are used to measure the association between each ADR and AED. The resulting list of signaled ADRs for each AED is validated against a widely used ADR database, called Side Effect Resource, in terms of the precision of ADR detection. The validation results indicate the validity of online health community data for the detection of AED ADRs. Furthermore, the lists of signaled AED ADRs are analyzed to answer questions related to the common ADRs of AEDs and the similarities between AEDs in terms of their signaled ADRs. The consistency of the drawn answers with the existing pharmaceutical knowledge suggests the utility of the data from online health communities for AED-related knowledge discovery tasks.

Complex Plithogenic Set

In the last decade uncertainty analysis in multi-attributes data sets is considered as one of the major issues. Same time another issues arises due to conflict among experts for their multi-valued and its existence. To resolve these issues, the properties of Plithogenic set is somehow considered as helpful for representing the multi-valued attributes and its multiple opposites, non-opposites as well as neutrals values with its contradiction. The problem arises when these values changes at given phase of time. Hence the current paper focused on introducing Complex Plithogenic set and its graphs for knowledge discovery and representation tasks with an illustrative example.

FIMA: A New Fuzzy Item sets Miner Algorithm

Frequent itemsets mining is the second step of Association rules mining which is the main task of Knowledge Discovery (KDD) and data mining (DM). There are three types of itemsets; Crisp (CI), Generalized (GI), and Fuzzy itemsets (FI). FI is the most recent type. This paper presents a new algorithm to mine fuzzy itemsets from large taxonomic databases depending on fuzzy taxonomies that reflect partial belongings among data items, also it depends on Item-Transaction layout, and shortest path finding between an item and its super classes. The proposed algorithm, Fuzzy Itemsets Miner Algorithm (FIMA) deals with the three types of fuzzy itemsets; taxonomic nodes, linguistic terms, and hedges. FIMA scans the database, under mining, only once. It excludes the need for complicated data structures, prunes the pruning steps of available algorithm, and avoids the weakness of manipulating low levels values of minimum support threshold. The algorithm performs much better than the available algorithm such that it reduces the complexity of mining FIs from exponential, O(an), to linear order of magnitude O(n).

A Critical Study on Stability Measures of Feature Selection with a Novel Extension of Lustgarten Index

Stability of feature selection algorithm refers to its robustness to the perturbations of the training set, parameter settings or initialization. A stable feature selection algorithm is crucial for identifying the relevant feature subset of meaningful and interpretable features which is extremely important in the task of knowledge discovery. Though there are many stability measures reported in the literature for evaluating the stability of feature selection, none of them follows all the requisite properties of a stability measure. Among them, the Kuncheva index and its modifications, are widely used in practical problems. In this work, the merits and limitations of the Kuncheva index and its existing modifications (Lustgarten, Wald, nPOG/nPOGR, Nogueira) are studied and analysed with respect to the requisite properties of stability measure. One more limitation of the most recent modified similarity measure, Nogueira’s measure, has been pointed out. Finally, corrections to Lustgarten’s measure have been proposed to define a new modified stability measure that satisfies the desired properties and overcomes the limitations of existing popular similarity based stability measures. The effectiveness of the newly modified Lustgarten’s measure has been evaluated with simple toy experiments.

Conventional displays of structures in data compared with interactive projection-based clustering (IPBC)

Clustering is an important task in knowledge discovery with the goal to identify structures of similar data points in a dataset. Here, the focus lies on methods that use a human-in-the-loop, i.e., incorporate user decisions into the clustering process through 2D and 3D displays of the structures in the data. Some of these interactive approaches fall into the category of visual analytics and emphasize the power of such displays to identify the structures interactively in various types of datasets or to verify the results of clustering algorithms. This work presents a new method called interactive projection-based clustering (IPBC). IPBC is an open-source and parameter-free method using a human-in-the-loop for an interactive 2.5D display and identification of structures in data based on the user’s choice of a dimensionality reduction method. The IPBC approach is systematically compared with accessible visual analytics methods for the display and identification of cluster structures using twelve clustering benchmark datasets and one additional natural dataset. Qualitative comparison of 2D, 2.5D and 3D displays of structures and empirical evaluation of the identified cluster structures show that IPBC outperforms comparable methods. Additionally, IPBC assists in identifying structures previously unknown to domain experts in an application.

Clustering Based on Supervised Learning of Exemplar Discriminative Information

In machine learning and data mining applications, clustering is a critical task for knowledge discovery that attract attentions from large quantities of researchers. Generally, with the help of label information, supervised learning methods have more flexible structure and better result than unsupervised learning. However, supervised learning is infeasible for clustering task. In this paper, to fill the gap between clustering and supervised learning, the proposed clustering methods introduce the exemplars discriminative information into a supervised learning. To build the effective objective function, a strategy for reducing intracluster distance and increasing intercluster distance is introduced to form a unified optimization objective function. With initially setting the clustering centers, the data that near the centers are selected as exemplars to indicate the ground truth of different classes. Discriminative learning is then introduced to learn the partition hyperplane and classify all the data into different classes. New clustering centers are calculated for selecting new exemplars alternately. Using the proposed algorithms, the unsupervised $K$ -means clustering problem is effectively solved from the perceptive of optimization. Feature mapping is also introduced to improve the performance by reducing the intercluster distance. A novel framework for exploring discriminative information from unsupervised data is provided. The proposed algorithms outperform the state-of-the-art approaches on a wide range of benchmark datasets in terms of accuracy.

A computational ecosystem to support eHealth Knowledge Discovery technologies in Spanish.

The massive amount of biomedical information published online requires the development of automatic knowledge discovery technologies to effectively make use of this available content. To foster and support this, the research community creates linguistic resources, such as annotated corpora, and designs shared evaluation campaigns and academic competitive challenges. This work describes an ecosystem that facilitates research and development in knowledge discovery in the biomedical domain, specifically in Spanish language. To this end, several resources are developed and shared with the research community, including a novel semantic annotation model, an annotated corpus of 1045 sentences, and computational resources to build and evaluate automatic knowledge discovery techniques. Furthermore, a research task is defined with objective evaluation criteria, and an online evaluation environment is setup and maintained, enabling researchers interested in this task to obtain immediate feedback and compare their results with the state-of-the-art. As a case study, we analyze the results of a competitive challenge based on these resources and provide guidelines for future research. The constructed ecosystem provides an effective learning and evaluation environment to encourage research in knowledge discovery in Spanish biomedical documents.

A framework for dependency estimation in heterogeneous data streams

Estimating dependencies from data is a fundamental task of Knowledge Discovery. Identifying the relevant variables leads to a better understanding of data and improves both the runtime and the outcomes of downstream Data Mining tasks. Dependency estimation from static numerical data has received much attention. However, real-world data often occurs as heterogeneous data streams: On the one hand, data is collected online and is virtually infinite. On the other hand, the various components of a stream may be of different types, e.g., numerical, ordinal or categorical. For this setting, we propose Monte Carlo Dependency Estimation (MCDE), a framework that quantifies multivariate dependency as the average statistical discrepancy between marginal and conditional distributions, via Monte Carlo simulations. MCDE handles heterogeneity by leveraging three statistical tests: the Mann–Whitney U, the Kolmogorov–Smirnov and the Chi-Squared test. We demonstrate that MCDE goes beyond the state of the art regarding dependency estimation by meeting a broad set of requirements. Finally, we show with a real-world use case that MCDE can discover useful patterns in heterogeneous data streams.

A hybrid quantum feature selection algorithm using a quantum inspired graph theoretic approach

Quantum machine learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. It generally exposes the synthesis of important machine learning algorithms in a quantum framework. Dimensionality reduction of a dataset with a suitable feature selection strategy is one of the most important tasks in knowledge discovery and data mining. The efficient feature selection strategy helps to improve the overall accuracy of a large dataset in terms of machine learning operations. In this paper, a quantum feature selection algorithm using a graph-theoretic approach has been proposed. The proposed algorithm has used the concept of correlation coefficient based graph-theoretic classical approach initially and then applied the quantum Oracle with CNOT operation to verify whether the dataset is suitable for dimensionality reduction or not. If it is suitable, then our algorithm can efficiently estimate their high correlation values by using quantum parallel amplitude estimation and amplitude amplification techniques. This paper also shows that our proposed algorithm substantially outperforms than some popular classical feature selection algorithms for supervised classification in terms of query complexity of $O(\frac {k\sqrt {N_{c}^{(k)}N_{f}^{(k)}}}{\epsilon })$, where N is the size of the feature vectors whose values are ⩾ THmin(minimum threshold), k is the number of iterations and where 𝜖 is the error for estimating those feature vectors. Compared with the classical counterpart, i.e. the performance of our quantum algorithm quadratically improves than others.

RCorp: a resource for chemical disease semantic extraction in Chinese

BackgroundTo robustly identify synergistic combinations of drugs, high-throughput screenings are desirable. It will be of great help to automatically identify the relations in the published papers with machine learning based tools. To support the chemical disease semantic relation extraction especially for chronic diseases, a chronic disease specific corpus for combination therapy discovery in Chinese (RCorp) is manually annotated.MethodsIn this study, we extracted abstracts from a Chinese medical literature server and followed the annotation framework of the BioCreative CDR corpus, with the guidelines modified to make the combination therapy related relations available. An annotation tool was incorporated to the standard annotation process.ResultsThe resulting RCorp consists of 339 Chinese biomedical articles with 2367 annotated chemicals, 2113 diseases, 237 symptoms, 164 chemical-induce-disease relations, 163 chemical-induce-symptom relations, and 805 chemical-treat-disease relations. Each annotation includes both the mention text spans and normalized concept identifiers. The corpus gets an inter-annotator agreement score of 0.883 for chemical entities, 0.791 for disease entities which are measured by F score. And the F score for chemical-treat-disease relations gets 0.788 after unifying the entity mentions.ConclusionsWe extracted and manually annotated a chronic disease specific corpus for combination therapy discovery in Chinese. The result analysis of the corpus proves its quality for the combination therapy related knowledge discovery task. Our annotated corpus would be a useful resource for the modelling of entity recognition and relation extraction tools. In the future, an evaluation based on the corpus will be held.