Akebo Yamakami

Contributions to the study of SMS spam filtering: new collection and results

The growth of mobile phone users has lead to a dramatic increasing of SMS spam messages. In practice, fighting mobile phone spam is difficult by several factors, including the lower rate of SMS that has allowed many users and service providers to ignore the issue, and the limited availability of mobile phone spam-filtering software. On the other hand, in academic settings, a major handicap is the scarcity of public SMS spam datasets, that are sorely needed for validation and comparison of different classifiers. Moreover, as SMS messages are fairly short, content-based spam filters may have their performance degraded. In this paper, we offer a new real, public and non-encoded SMS spam collection that is the largest one as far as we know. Moreover, we compare the performance achieved by several established machine learning methods. The results indicate that Support Vector Machine outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison.

The shortest path problem on networks with fuzzy parameters

Shortest path problem where the costs have vague values is one of the most studied problems in fuzzy sets and systems area. However, due to its high computational complexity, previously published algorithms present peculiarities and problems that need to be addressed (e.g. they find costs without an existing path, they determine a fuzzy solution set but do not give any guidelines to help the decision-maker choose the best path, they can only be applied in graphs with fuzzy non-negative parameters, etc.). In this paper, one proposes an iterative algorithm that assumes a generic ranking index for comparing the fuzzy numbers involved in the problem, in such a way that each time in which the decision-maker wants to solve a concrete problem (s)he can choose (or propose) the ranking index that best suits that problem. This algorithm, that solves the above remarked drawbacks, is based on the Ford-Moore-Bellman algorithm for classical graphs, and in concrete it can be applied in graphs with negative parameters and it can detect whether there are negative circuits. For the sake of illustrating the performance of the algorithm in the paper, it has been here developed using only certain order relations, but it is not restricted at all to use these comparison relations exclusively. The proposed algorithm is easy of understanding as the theoretical base of a decision support system oriented to solving this kind of problems.

Spam filtering: how the dimensionality reduction affects the accuracy of Naive Bayes classifiers

E-mail spam has become an increasingly important problem with a big economic impact in society. Fortunately, there are different approaches allowing to automatically detect and remove most of those messages, and the best-known techniques are based on Bayesian decision theory. However, such probabilistic approaches often suffer from a well-known difficulty: the high dimensionality of the feature space. Many term-selection methods have been proposed for avoiding the curse of dimensionality. Nevertheless, it is still unclear how the performance of Naive Bayes spam filters depends on the scheme applied for reducing the dimensionality of the feature space. In this paper, we study the performance of many term-selection techniques with several different models of Naive Bayes spam filters. Our experiments were diligently designed to ensure statistically sound results. Moreover, we perform an analysis concerning the measurements usually employed to evaluate the quality of spam filters. Finally, we also investigate the benefits of using the Matthews correlation coefficient as a measure of performance.

Stabilization of continuous and discrete linear systems subjected to control structure constraints

In this paper we present a simple procedure for obtaining stabilization of a class of continuous and discrete linear systems subjected to control structure constraints, giving more attention to the so-called decentralized control. Contrary to the continuous case where much work has been done, the solution presented here for the discrete case is one of the first to be presented. Some examples are treated and, for the continuous case, some comparisons are made with other methods found in the literature.

Content-based spam filtering

The growth of email users has resulted in the dramatic increasing of the spam emails. Helpfully, there are different approaches able to automatically detect and remove most of these messages, and the best-known ones are based on Bayesian decision theory and Support Vector Machines. However, there are several forms of Naive Bayes filters, something the anti-spam literature does not always acknowledge. In this paper, we discuss seven different versions of Naive Bayes classifiers, and compare them with the well-known Linear Support Vector Machine on six non-encoded datasets. Moreover, we propose a new measurement in order to evaluate the quality of anti-spam classifiers. In this way, we investigate the benefits of using Matthews correlation coefficient as the measure of performance.

Devising adaptive migration policies for cooperative distributed genetic algorithms

Distributed genetic algorithms (DGAs) constitute an interesting approach to undertake the premature convergence problem in evolutionary optimization. This is done by spatial partitioning a huge panmitic population into several semi-isolated groups, called demes, each evolving in parallel by its own pace, and possibly exploring different regions of the search space. At the center of such approach lies the migratory process that simulates the swapping of individuals belonging to different demes, in such a way to ensure the sharing of good genetic material. In this paper, we model the migration step in DGAs as an explicit means to promote cooperation among genetic agents, autonomous entities encapsulating GA instances for possibly tackling different sub-problems of a complicated task. The focus is on the characterization of adaptive migration policies in which the choice of what individuals to migrate and/or replace is not defined a priori but according to a more knowledge-oriented rule. Comparative results obtained for a data-mining task were conducted, in order to assess the performance of adaptive migration according to efficiency/effectiveness criteria.

Evaluation of Approaches for Dimensionality Reduction Applied with Naive Bayes Anti-Spam Filters

There are different approaches able to automatically detect e-mail spam messages, and the best-known ones are based on Bayesian decision theory. However, the most of these approaches have the same difficulty: the high dimensionality of the feature space. Many term selection methods have been proposed in the literature. Nevertheless, it is still unclear how the performance of naive Bayes anti-spam filters depends on the methods applied for reducing the dimensionality of the feature space. In this paper, we compare the performance of most popular methods used as term selection techniques, such as document frequency, information gain, mutual information, X 2 statistic, and odds ratio used for reducing the dimensionality of the term space with four well-known different versions of naive Bayes spam filter.

Two-phase method to solve fuzzy quadratic programming problems

Quadratic programming problems are of up most importance in a variety of relevant practical fields, as e.g., portfolio selection. This work presents and develops an original and novel fuzzy sets based method that solves a class of quadratic programming problems with vagueness in the set of constraints. The method uses two phases to solve fuzzy quadratic programming problems, which eventually can be considered in the portfolio context. In the first phase we parametrize the fuzzy problem in several classical alpha-problems with different cutting levels. In the second phase each of these alpha-problems is solved by using conventional solving techniques. The final fuzzy solution to the former problem can be obtained by integrating all of these particular alpha-solutions. Some illustrative numerical examples illustrating the solution approach are solved and analyzed to show the efficiency of this proposed method.

Facing the spammers: A very effective approach to avoid junk e-mails

Spam has become an increasingly important problem with a big economic impact in society. Spam filtering poses a special problem in text categorization, in which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. In this paper, we present a novel approach to spam filtering based on the minimum description length principle and confidence factors. The proposed model is fast to construct and incrementally updateable. Furthermore, we have conducted an empirical experiment using three well-known, large and public e-mail databases. The results indicate that the proposed classifier outperforms the state-of-the-art spam filters.

Filtering spams using the minimum description length principle

Spam has become an increasingly important problem with a big economic impact in society. Spam filtering poses a special problem in text categorization, of which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. In this paper, we present a novel approach to spam filtering based on the minimum description length principle. The proposed model is fast to construct and incrementally updateable. Additionally, we offer an analysis concerning the measurements usually employed to evaluate the quality of the anti-spam classifiers. In this sense, we present a new measure in order to provide a fairer comparison. Furthermore, we conducted an empirical experiment using six well-known, large and public databases. Finally, the results indicate that our approach outperforms the state-of-the-art spam filters.