An email classification model based on rough set theory

The communication via email is one of the most popular services of the Internet. Emails have brought us great convenience in our daily work and life. However, unsolicited messages or spam, flood our email boxes, which results in bandwidth, time and money wasting. To this end, this paper presents a rough set based model to classify emails into three categories - spam, no-spam and suspicious, rather than two classes (spam and non-spam) in most currently used approaches. By comparing with popular classification methods like Naive Bayes classification, the error ratio that a non-spam is discriminated to spam can be reduced using our proposed model.<br /

Revealing cell cycle control by combining model-based detection of periodic expression with novel cis-regulatory descriptors

<p>Abstract</p> <p>Background</p> <p>We address the issue of explaining the presence or absence of phase-specific transcription in budding yeast cultures under different conditions. To this end we use a model-based detector of gene expression periodicity to divide genes into classes depending on their behavior in experiments using different synchronization methods. While computational inference of gene regulatory circuits typically relies on expression similarity (clustering) in order to find classes of potentially co-regulated genes, this method instead takes advantage of known time profile signatures related to the studied process.</p> <p>Results</p> <p>We explain the regulatory mechanisms of the inferred periodic classes with <it>cis</it>-regulatory descriptors that combine upstream sequence motifs with experimentally determined binding of transcription factors. By systematic statistical analysis we show that periodic classes are best explained by combinations of descriptors rather than single descriptors, and that different combinations correspond to periodic expression in different classes. We also find evidence for additive regulation in that the combinations of <it>cis</it>-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions. Finally, we demonstrate that our approach retrieves combinations that are more specific towards known cell-cycle related regulators than the frequently used clustering approach.</p> <p>Conclusion</p> <p>The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms. Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes.</p

Modelagem de Sistemas MIMO baseada em Conjuntos Aproximados.

A modelagem de sistemas dinâmicos é de grande importância em vários campos das ciências e engenharias. Os métodos clássicos de modelagem de sistemas são baseados em modelos matemáticos acurados. Entretanto, para sistemas complexos com características não lineares e/ou parâmetros variantes no tempo, a obtenção dos modelos correspondentes não é uma tarefa trivial. Os modelos baseados em regras têm uma característica importante em relação aos modelos gerados pelos métodos estatísticos clássicos e os modelos gerados por intermédio de redes neurais artificiais. Essa característica consiste na facilidade com que o conhecimento embutido em cada modelo pode ser interpretado, visto que o conhecimento está todo abstraído em regras cuja sintaxe é muito próxima da linguagem humana. Essa característica confere a esses modelos a capacidade de serem facilmente inseridos em sistemas computacionais gerais. Diferentemente dos métodos de análise de dados convencionais, que frequentemente utilizam procedimentos estatísticos, abordagens com conjuntos aproximados são baseadas em técnicas de mineração de dados visando à extração de conhecimentos (Tay e Shen, 2002). A Teoria dos Conjuntos Aproximados (Rough Sets) foi introduzida por Pawlak com dois objetivos principais: revelar estruturas pertinentes em conjuntos de dados e classificar objetos (Pawlak, 1982). Através de uma revisão bibliográfica, verificou-se que não é comum trabalhos sobre conjuntos aproximados abordando questões relacionadas com a modelagem de sistemas estáticos ou dinâmicos que utilizam variáveis contínuas ou amostradas, principalmente em relação a sistemas lineares e não lineares, em especial para sistemas com múltiplas variáveis de entrada e de saída (sistemas MIMO) (Pinheiro 2009, Pinheiro et al., 2010a). Então, neste trabalho será apresentada uma proposta para construção de modelos baseados em regras de sistema MIMO. Para a validação da modelagem, serão apresentados exemplos de modelos não lineares com duas variáveis de entrada e duas de saída, de um modelo discreto e um modelo contínuo de um sistema de nível real (tanques acoplados). Para fins de comparação com a modelagem proposta, serão utilizados modelos fuzzy Takagi-Sugeno com estruturas do tipo ANFIS (Adaptative Network Based Fuzzy Inference System) (Jang 1993)

Synergies between machine learning and reasoning - An introduction by the Kay R. Amel group

This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been developed quite separately in the last four decades. First, some common concerns are identified and discussed such as the types of representation used, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding. Then, the survey is organised in seven sections covering most of the territory where KRR and ML meet. We start with a section dealing with prototypical approaches from the literature on learning and reasoning: Inductive Logic Programming, Statistical Relational Learning, and Neurosymbolic AI, where ideas from rule-based reasoning are combined with ML. Then we focus on the use of various forms of background knowledge in learning, ranging from additional regularisation terms in loss functions, to the problem of aligning symbolic and vector space representations, or the use of knowledge graphs for learning. Then, the next section describes how KRR notions may benefit to learning tasks. For instance, constraints can be used as in declarative data mining for influencing the learned patterns; or semantic features are exploited in low-shot learning to compensate for the lack of data; or yet we can take advantage of analogies for learning purposes. Conversely, another section investigates how ML methods may serve KRR goals. For instance, one may learn special kinds of rules such as default rules, fuzzy rules or threshold rules, or special types of information such as constraints, or preferences. The section also covers formal concept analysis and rough sets-based methods. Yet another section reviews various interactions between Automated Reasoning and ML, such as the use of ML methods in SAT solving to make reasoning faster. Then a section deals with works related to model accountability, including explainability and interpretability, fairness and robustness. Finally, a section covers works on handling imperfect or incomplete data, including the problem of learning from uncertain or coarse data, the use of belief functions for regression, a revision-based view of the EM algorithm, the use of possibility theory in statistics, or the learning of imprecise models. This paper thus aims at a better mutual understanding of research in KRR and ML, and how they can cooperate. The paper is completed by an abundant bibliography