Leticia Cagnina

Particle swarm optimization for sequencing problems: a case study

PSO has been successfully used in different areas (e. g. multidimensional and multiobjective optimization, neural networks training, etc.) but there are few reports on research in sequencing problems. In this paper we present a hybrid particle swarm optimizer (HPSO) that incorporates a random key representation for particles and a dynamic mutation operator similar to those used in evolutionary algorithm. This algorithm was designed with permutation problems. Our preliminary study shows the algorithm performance when it is applied to a set of instances for the total weighted tardiness problem in single machine environments. Results show that the hybrid HPSO is a promising approach to solve sequencing problems.

Solving constrained optimization problems with a hybrid particle swarm optimization algorithm

This article presents a particle swarm optimization algorithm for solving general constrained optimization problems. The proposed approach introduces different methods to update the particles information, as well as the use of a double population and a special shake mechanism designed to avoid premature convergence. It also incorporates a simple constraint-handling technique. Twenty-four constrained optimization problems commonly adopted in the evolutionary optimization literature, as well as some structural optimization problems are adopted to validate the proposed approach. The results obtained by the proposed approach are compared with respect to those generated by algorithms representative of the state of the art in the area.

Measuring the quality of web content using factual information

Nowadays, many decisions are based on information found in the Web. For the most part, the disseminating sources are not certified, and hence an assessment of the quality and credibility of Web content became more important than ever. With factual density we present a simple statistical quality measure that is based on facts extracted from Web content using Open Information Extraction. In a first case study, we use this measure to identify featured/good articles in Wikipedia. We compare the factual density measure with word count, a measure that has successfully been applied to this task in the past. Our evaluation corroborates the good performance of word count in Wikipedia since featured/good articles are often longer than non-featured. However, for articles of similar lengths the word count measure fails while factual density can separate between them with an F-measure of 90.4%. We also investigate the use of relational features for categorizing Wikipedia articles into featured/good versus non-featured ones. If articles have similar lengths, we achieve an F-measure of 86.7% and 84% otherwise.

An efficient Particle Swarm Optimization approach to cluster short texts

Short texts such as evaluations of commercial products, news, FAQs and scientific abstracts are important resources on the Web due to the constant requirements of people to use this on line information in real life. In this context, the clustering of short texts is a significant analysis task and a discrete Particle Swarm Optimization (PSO) algorithm named CLUDIPSO has recently shown a promising performance in this type of problems. CLUDIPSO obtained high quality results with small corpora although, with larger corpora, a significant deterioration of performance was observed. This article presents CLUDIPSO^*, an improved version of CLUDIPSO, which includes a different representation of particles, a more efficient evaluation of the function to be optimized and some modifications in the mutation operator. Experimental results with corpora containing scientific abstracts, news and short legal documents obtained from the Web, show that CLUDIPSO^* is an effective clustering method for short-text corpora of small and medium size.

A bi-population PSO with a shake-mechanism for solving constrained numerical optimization

This paper presents an enhanced particle swarm optimizer approach, which is designed to solve numerical constrained optimization problems. The approach uses a single method to handle different types of constraints (linear, nonlinear, equality or inequality) and it incorporates a shake- mechanism and a dual population in an attempt to overcome the problem of premature convergence to local optima. The proposed algorithm is validated using standard test functions taken from the specialized literature and is compared with respect to algorithms representative of the state-of-the-art in the area. Our preliminary results indicate that our proposed approach is a highly competitive alternative to solve constrained optimization problems.

A particle swarm optimizer for constrained numerical optimization

This paper presents a particle swarm optimizer to solve constrained optimization problems. The proposed approach adopts a simple method to handle constraints of any type (linear, nonlinear, equality and inequality), and it also presents a novel mechanism to update the velocity and position of each particle. The approach is validated using standard test functions reported in the specialized literature and its compared with respect to algorithms representative of the state-of-the-art in the area. Our results indicate that the proposed scheme is a promising alternative to solve constrained optimization problems using particle swarm optimization.

A fast particle swarm algorithm for solving smooth and non-smooth economic dispatch problems

The Economic Dispatch (ED) problem is a common task in the operational planning of a power system, which requires to be optimized. Such optimization includes two goals: (1) the minimization of the total scheduled cost and (2) the minimization of the response time. This article presents a fast Particle Swarm Optimization (PSO) approach for solving the ED problem. The proposed approach is applied to five case studies, having different size and complexity. Three of them have a smooth fuel cost, whereas the other two have non-smooth load functions, with valve-points. The experimental results indicate that the proposed approach provides good results on the test problems adopted, while requiring a lower computational time than other approaches taken from the specialized literature.

Detecting Deceptive Opinions: Intra and Cross-Domain Classification Using an Efficient Representation

Online opinions play an important role for customers and companies because of the increasing use they do to make purchase and business decisions. A consequence of that is the growing tendency to post fake reviews in order to change purchase decisions and opinions about products and services. Therefore, it is really important to filter out deceptive comments from the retrieved opinions. In this paper we propose the character n-grams in tokens, an efficient and effective variant of the traditional character n-grams model, which we use to obtain a low dimensionality representation of opinions. A Support Vector Machines classifier was used to evaluate our proposal on available corpora with reviews of hotels, doctors and restaurants. In order to study the performance of our model, we make experiments with intra and cross-domain cases. The aim of the latter experiment is to evaluate our approach in a realistic cross-domain scenario where deceptive opinions are available in a domain but not in another one. After comparing our method with state-of-the-art ones we may conclude that using character n-grams in tokens allows to obtain competitive results with a low dimensionality representation.

Classification of deceptive opinions using a low dimensionality representation

Opinions in social media play such an important role for customers and companies that there is a growing tendency to post fake reviews in order to change purchase decisions and opinions. In this paper we propose the use of different features for a low dimension representation of opinions. We evaluate our proposal incorporating the features to a Support Vector Machines classifier and we use an available corpus with reviews of hotels in Chicago. We perform comparisons with previous works and we conclude that using our proposed features it is possible to obtain competitive results with a small amount of features for representing the data. Finally, we also investigate if the use of emotions can help to discriminate between truthful and deceptive opinions as previous works show to happen for deception detection in text in general.

Deception Detection and Opinion Spam

In this chapter we first introduce the reader to the problem of deception detection in general, describing how lies may be detected automatically using different methods. Later we address the specific problem of deception detection in predatory communication. We make emphasis especially on those approaches using affective resources as categorical and psychometric information provided by natural language processing tools. Finally, we focus on the problem of opinion spam whose detection is very important for reliable opinion mining. In fact, nowadays a large number of opinion reviews are posted on the Web. Such reviews are a very important source of information for customers and companies. Unfortunately, due to the business behind it, there is an increasing number of deceptive opinions on the Web. Those opinions are fictitious and have been deliberately written to sound authentic in order to deceive the consumers promoting a low quality product (positive deceptive opinions) or criticizing a potentially good quality one (negative deceptive opinions). Then, we summary some interesting approaches to detect spam opinion on the Web.