Intelligent Data Analysis: An International Journal

As a combinatorial optimization problem, feature selection has been widely used in machine learning and data mining. In this paper, a feature selection method using forest optimization algorithm based on contribution degree is proposed. The proposed method uses a contribution degree strategy which is embedded in forest optimization algorithm. The goal of the contribution degree is to guide the search process of the forest optimization algorithm to select features according to high class correlation and low redundancy between features. The proposed algorithm is verified on some data sets from the UCI repository and the experiments show that the proposed method improves the classification accuracy compared with some other methods.

In today’s rapidly emerging computing environment, cloud computing has become a significant trend for the delivery of IT business services, and representes a potential technology resource choice that offers cost effective and scalable processing. However, Distributed Denial of Service (DDoS) attacks continually target cloud services and resource availability, rendering the cloud unavailable to the detriment of both cloud providers and users. In previous research, feature selection, has revealed its importance in the recognition of irrelevant and redundant features, which increases detection rates and decreases processing speeds toward the evaluation of intrusive patterns, while reducing computational complexity. In this work we propose a Hybrid Filter-Wrapper Feature Selection HFWFS method for DDoS detection, which takes advantage of both filter and wrapper methods, to identify the most irrelevant and redundant features in order to form a reduced input subset. Subsequently, it applies a wrapper method to achieve the optimal selection of features. To evaluate the performance of our proposed model, we used two datasets (NSL-KDD and UNSW-NB15) and a Random Tree classifier. The results indicated that the proposed model may reduce the number of features from more than 40 to nine, while maintaining high detection accuracy, in contrast to well-known feature selection methods.

As an efficient sampling algorithm for latent dirichlet allocation SparseLDA uses cache strategy to improve the time and space efficiency of its standard gibbs sampling algorithm (StdGibbs) by recycling previous computation. However, SparseLDA cannot further improve the time-efficiency of StdGibbs, since the amount of recycled computation is limited. This is because the word types of two adjacent tokens are usually different and the previous computation cannot be further recycled easily. To solve this problem, in this paper we propose a new algorithm named Efficient SparseLDA (ESparseLDA) based on SparseLDA. The main idea of ESparseLDA is to first rearrange the tokens within one text according to the word types so that the tokens of the same word type are aggregated together and then recycle more computation while making no approximation and ensuring the exactness. In this paper, we make detailed theoretical explanations and comparative experimental analyses on the correctness, exactness and time-efficiency of ESparseLDA. In detail, the statistical significance tests on perplexities strictly show that ESparseLDA is correct and exact. In addition, the running time results show that the time-efficiency of ESparseLDA is the higher than SparseLDA in varying degrees from 5.06% to 31.85% on the different datasets used in experiments.

Data is shared among different organizations for mutual benefit. Data mining techniques are utilized to discover valuable knowledge for decision-making. However, data mining poses a threat to disclose the sensitive information. Thus, the sensitive knowledge should be concealed before releasing data. The pervious works either address the association rule or utility itemsets hiding problem. This paper focuses on preserving the sensitive utility and frequent itemsets, and a sanitization approach named HUFI is presented. The sensitive itemsets are hidden by reducing their support or utility below the minimum thresholds. For a sensitive itemset, the concept of maximum boundary value is introduced to determine the hidden strategy. Then, a transaction supporting minimal number of non-sensitive itemsets is selected to be sanitized. In such a transaction, a weight is assigned to each item contained in the sensitive itemset, and an item with the highest weight is selected to be modified. We compared HUFI with the state of the art algorithms on various databases. The experiment results show that HUFI outperforms the other algorithms in minimizing the side effects on non-sensitive knowledge and maintaining the database quality after the sanitization process. In addition, the impact of database density on sanitization approaches is observed.

Directed networks find many applications in computer science, social science and biomedicine, among others. In this paper we propose a new graph mining algorithm that is capable of locating all frequent induced subgraphs in a given set of directed networks. We present an incremental coding scheme for representing the canonical form of a graph, study its properties, and develop new techniques for pattern generation suitable for directed networks. We prove that our algorithm is complete, meaning that no qualified pattern is missed by the algorithm. Furthermore, our algorithm is correct in the sense that all patterns found by the algorithm are frequent induced subgraphs in the given networks. Experimental results based on synthetic data and gene regulatory networks show the good performance of our algorithm, and its application in network inference.

In this paper we present a new c-means clustering algorithm for combined continuous-nominal data. We use spherical representation of nominal data. The impact of specific features is modeled with corresponding weights in metric definition. To solve constrained minimization problem we transfer the methodology of reformulation functions to the considered context. As a result we obtain a clustering algorithm with adaptation of weights. Series of numerical experiments on real and synthetic data show that the algorithm can successfully cluster raw, non-normalized data.

The ontology language OWL 2 EL, is designed for knowledge modeling and has been widely used in real applications. However, modeling knowledge as OWL ontologies is an error-prone process, where logical errors or contradictions would be imported. Further, it is almost impossible to manually find errors occurring in large-scale ontologies. Finding justification, an important service for error pinpointing, has then attracted much attention of researchers and developers. However, current methods of finding justifications suffer from an issue: high-coupling of reasoners, i.e., there is a tight relation between reasoners and the task of finding justifications. This makes the performance of finding justifications be highly influenced by the utilized reasoner, and, it is also restricted to optimize the algorithms effectively. In order to tackle this problem, we consider giving a method such that the task of finding justifications is independent from the utilized reasoner. Specifically, we propose a kind of graph called Explanation Dependency Graph (EDG) which guides to compute justifications from the reasoning results directly. We further give several optimizing strategies and prove the correctness of our method. We implement our method and evaluate it on real ontologies, including SNOMED CT. The experimental results show that our method is practical and performs better than current methods.

With the development of the Semantic Web, more and more semantic data including many useful knowledge bases has been published on the Web. Such knowledge bases always lack expressive schema information, especially disjointness axioms and subclass axioms. This makes it difficult to perform many critical Semantic Web tasks like ontology reasoning, inconsistency handling and ontology mapping. To deal with this problem, a few approaches have been proposed to generate terminology axioms. However, they often adopt the closed world assumption which is opposite to the assumption adopted by the semantic data. This may lead to a lot of noisy negative examples so that existing learning approaches fail to perform well on such incomplete data. In this paper, a novel framework is proposed to automatically obtain disjointness axioms and subclass axioms from incomplete semantic data. This framework first obtains probabilistic type assertions by exploiting a type inference algorithm. Then a mining approach based on association rule mining is proposed to learn high-quality schema information. To address the incompleteness problem of semantic data, the mining model introduces novel definitions to compute the support and confidence for pruning false axioms. Our experimental evaluation shows promising results over several real-life incomplete knowledge bases like DBpedia and LUBM by comparing with existing relevant approaches.

The Acute Kidney Injury (AKI), is a disease that affects the kidneys and is characterized by the rapid deterioration of these organs, usually associated with a pre-existing critical illness. Being an acute disease, time is a key element in the prevention. By anticipating a patient’s state transition, we are preventing future complications in his health, such as the development of a chronic disease or loss of an organ, in addition to decreasing the amount of money spent on the patient’s care.

The main goal of this paper is to address the problem of correctly predicting the illness path in various patients by studying different methodologies to predict this disease and propose new distinct approaches based on this idea of improving the performance of the classification.

Through the comparison of five different approaches (Markov Chain Model ICU Specialists, Markov Chain Model Features, Markov Chain Model Conditional Features, Markov Chain Model and Random Forest), we came to the conclusion that the application of conditional probabilities to this problem produces a more accurate prediction, based on common inputs.

Recently how to recommend celebrities to public has become an interesting problem in real social network applications. In this problem, a matrix of users’ following actions is usually very sparse. Therefore, it causes conventional collaborative filtering based methods to degrade significantly in recommendation performance. To address the sparsity problem, side information could be rendered. Collaborative social topic regression (CSTR) is an appealing new method, which combines the matrix of general users’ following actions, content information and a social network of celebrities. However, this method is limited by using the topic model latent Dirichlet allocation (LDA) as the critical component. The learned content representation may not be compact and effective enough. Moreover, the social network of general users also exists, which is helpful for recommendation. In this paper, we employ a deep learning component to learn more effective feature representation and incorporate social network information of general users by adding social regularization terms. We propose a novel hierarchical Bayesian model named collaborative social deep learning (CSDL), which jointly handles deep learning for the content information and collaborative filtering for general users’ following actions, the social network of celebrities and that of general users. Experiments on two real-world datasets show the effectiveness of our proposed model.

Many researches have exploited textual data, such as news, online blogs, and financial reports, in order to predict stock price movements effectively. Previous studies formed the task as a classification problem predicting upward or downward movement of stock prices from text documents. Such an approach, however, may be deemed inappropriate when combined with sentiment analysis. In financial documents, same words may convey different sentiments across different sectors; if documents from multiple sectors are learned simultaneously, performance can deteriorate. Therefore, we conducted sentiment analysis of 8-K financial reports of firms sector by sector. In particular, we also employed distributed representation for predicting stock price movements. Experiment results show that our approach improves prediction performance by 25.4% over the baseline model, and that the direction of post-announcement stock price movements shifts accordingly with the polarity of the sentiment of reports. Not only does our model improve predictability, but also provides visualizations, which may assist agents actively trading in the field with understanding the drivers for the observed stock movements. The two main aspects of our model, predictability and interpretability, will provide meaningful insights to help decision-makers in the industry with time-split trading decisions or data-driven detection of promising companies.

Data needs to be released to the relevant decision makers and researchers. Privacy protection should be carried out first because it contains personal sensitive information. The k -anonymity algorithm is an important privacy protection algorithm, and partitioning is one of its key methods. To reduce the computational complexity and low speed of existing privacy-preserving algorithms for high-dimensional data publishing, a probabilistic optimal projection partition k -dimensional (KD)-tree k -anonymity algorithm is proposed. First, some attribute dimensions are probabilistically selected from the global domain. Then, for these dimensions, the partition coefficient is calculated and the optimal partition point is determined. Furthermore, an improved KD-tree structure is introduced in which a node is a collection rather than a data point. The proposed KD-tree node is divided into left and right child nodes by the hyper-plane passing through the dividing point and perpendicular to the optimal dimension. The proposed algorithm is validated by a theoretical analysis and comparison experiments. The results show that the proposed algorithm can reduce the average generalization range by 11% to 22% compared to traditional k -anonymity. This enables better division and better dataset availability. Moreover, the runtime is reduced by 8% to 32% compared to globally optimal projection partitioning k -anonymity.