Michelangelo Ceci

Bio: Michelangelo Ceci is an academic researcher from University of Bari. The author has contributed to research in topics: Cluster analysis & Spatial analysis. The author has an hindex of 28, co-authored 205 publications receiving 2395 citations. Previous affiliations of Michelangelo Ceci include Jožef Stefan Institute & Logica.

Classifying web documents in a hierarchy of categories: a comprehensive study

Abstract: Most of the research on text categorization has focused on classifying text documents into a set of categories with no structural relationships among them (flat classification). However, in many information repositories documents are organized in a hierarchy of categories to support a thematic search by browsing topics of interests. The consideration of the hierarchical relationship among categories opens several additional issues in the development of methods for automated document classification. Questions concern the representation of documents, the learning process, the classification process and the evaluation criteria of experimental results. They are systematically investigated in this paper, whose main contribution is a general hierarchical text categorization framework where the hierarchy of categories is involved in all phases of automated document classification, namely feature selection, learning and classification of a new document. An automated threshold determination method for classification scores is embedded in the proposed framework. It can be applied to any classifier that returns a degree of membership of a document to a category. In this work three learning methods are considered for the construction of document classifiers, namely centroid-based, naive Bayes and SVM. The proposed framework has been implemented in the system WebClassIII and has been tested on three datasets (Yahoo, DMOZ, RCV1) which present a variety of situations in terms of hierarchical structure. Experimental results are reported and several conclusions are drawn on the comparison of the flat vs. the hierarchical approach as well as on the comparison of different hierarchical classifiers. The paper concludes with a review of related work and a discussion of previous findings vs. our findings.

Discovery of spatial association rules in geo-referenced census data: A relational mining approach

Abstract: Census data mining has great potential both in business development and in good public policy, but still must be solved in this field a number of research issues. In this paper, problems related to the geo-referenciation of census data are considered. In particular, the accommodation of the spatial dimension in census data mining is investigated for the task of discovering spatial association rules, that is, association rules involving spatial relations among (spatial) objects. The formulation of a new method based on a multi-relational data mining approach is proposed. It takes advantage of the representation and inference techniques developed in the field of Inductive Logic Programming (ILP). In particular, the expressive power of predicate logic is profitably used to represent both spatial relations and background knowledge, such as spatial hierarchies and rules for spatial qualitative reasoning. The logical notions of generality order and of the downward refinement operator on the space of patterns are profitably used to define both the search space and the search strategy. The proposed method has been implemented in the ILP system SPADA (Spatial Pattern Discovery Algorithm). SPADA has been interfaced both to a module for the extraction of spatial features from a spatial database and to a module for numerical attribute discretization. The three modules have been used in an application to urban accessibility of a hospital in Stockport, Greater Manchester. Results obtained through a spatial analysis of geo-referenced census data are illustrated.

Top-down induction of model trees with regression and splitting nodes

Abstract: Model trees are an extension of regression trees that associate leaves with multiple regression models. In this paper, a method for the data-driven construction of model trees is presented, namely, the stepwise model tree induction (SMOTI) method. Its main characteristic is the induction of trees with two types of nodes: regression nodes, which perform only straight-line regression, and splitting nodes, which partition the feature space. The multiple linear model associated with each leaf is then built stepwise by combining straight-line regressions reported along the path from the root to the leaf. In this way, internal regression nodes contribute to the definition of multiple models and have a "global" effect, while straight-line regressions at leaves have only "local" effects. Experimental results on artificially generated data sets show that SMOTI outperforms two model tree induction systems, M5' and RETIS, in accuracy. Results on benchmark data sets used for studies on both regression and model trees show that SMOTI performs better than RETIS in accuracy, while it is not possible to draw statistically significant conclusions on the comparison with M5'. Model trees induced by SMOTI are generally simple and easily interpretable and their analysis often reveals interesting patterns.

Machine learning methods for automatically processing historical documents: from paper acquisition to XML transformation

Abstract: One of the aims of the EU project COLLATE is to design and implement a Web-based collaboratory for archives, scientists and end-users working with digitized cultural material. Since the originals of such a material are often unique and scattered in various archives, severe problems arise for their wide fruition. A solution would be to develop intelligent document processing tools that automatically transform printed documents into a Web-accessible form such as XML. Here, we propose the use of a document processing system, WISDOM++, which uses heavily machine learning techniques in order to perform such a task, and report promising results obtained in preliminary experiments.

CloFAST: closed sequential pattern mining using sparse and vertical id-lists

Abstract: Sequential pattern mining is a computationally challenging task since algorithms have to generate and/or test a combinatorially explosive number of intermediate subsequences. In order to reduce complexity, some researchers focus on the task of mining closed sequential patterns. This not only results in increased efficiency, but also provides a way to compact results, while preserving the same expressive power of patterns extracted by means of traditional (non-closed) sequential pattern mining algorithms. In this paper, we present CloFAST, a novel algorithm for mining closed frequent sequences of itemsets. It combines a new data representation of the dataset, based on sparse id-lists and vertical id-lists, whose theoretical properties are studied in order to fast count the support of sequential patterns, with a novel one-step technique both to check sequence closure and to prune the search space. Contrary to almost all the existing algorithms, which iteratively alternate itemset extension and sequence extension, CloFAST proceeds in two steps. Initially, all closed frequent itemsets are mined in order to obtain an initial set of sequences of size 1. Then, new sequences are generated by directly working on the sequences, without mining additional frequent itemsets. A thorough performance study with both real-world and artificially generated datasets empirically proves that CloFAST outperforms the state-of-the-art algorithms, both in time and memory consumption, especially when mining long closed sequences.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.