Nuno C. Marques

Determining the polarity of words through a common online dictionary

Considerable attention has been given to polarity of words and the creation of large polarity lexicons. Most of the approaches rely on advanced tools like part-of-speech taggers and rich lexical resources such as WordNet. In this paper we show and examine the viability to create a moderate-sized polarity lexicon using only a common online dictionary, five positive and five negative words, a set of highly accurate extraction rules, and a simple yet effective polarity propagation algorithm. The algorithm evaluation results show an accuracy of 84.86% for a lexicon of 3034 words.

An extension of self-organizing maps to categorical data

Self-organizing maps (SOM) have been recognized as a powerful tool in data exploratoration, especially for the tasks of clustering on high dimensional data. However, clustering on categorical data is still a challenge for SOM. This paper aims to extend standard SOM to handle feature values of categorical type. A batch SOM algorithm (NCSOM) is presented concerning the dissimilarity measure and update method of map evolution for both numeric and categorical features simultaneously.

Evolutionary feature selection for artificial neural network pattern classifiers

This paper presents FeaSANNT, an evolutionary procedure for feature selection and weight training for neural network classifiers. FeaSANNT exploits the global nature of evolutionary search to avoid sub-optimal peaks of performance. FeaSANNT was used to train a multi-layer perceptron classifier on seven benchmark problems. FeaSANNT attained accurate and consistent learning results, and significantly reduced the number of data attributes compared to four state-of-the-art standard filter and wrapper feature selection methods. Thanks to the robustness of evolutionary search, FeaSANNT did not require time-consuming re-tuning of the learning parameters for each test problem.

User group profile modeling based on user transactional data for personalized systems

In this paper, we propose a framework named UMT (User-profile Modeling based on Transactional data) for modeling user group profiles based on the transactional data. UMT is a generic framework for application systems that keep the historical transactions of their users. In UMT, user group profiles consist of three types: basic information attributes, synthetic attributes and probability distribution attributes. User profiles are constructed by clustering user transaction data and integrating cluster attributes with domain information extracted from application systems and other external data sources. The characteristic of UMT makes it suitable for personalization of transaction-based commercial application systems. A case study is presented to illustrate how to use UMT to create a personalized tourism system capable of using domain information in intelligent ways and of reacting to external events.

Extending Learning Vector Quantization for Classifying Data with Categorical Values

Learning vector quantization (LVQ) is a supervised neural network method applicable in non-linear separation problems and widely used for data classification. Existing LVQ algorithms are mostly focused on numerical data. This paper presents a batch type LVQ algorithm used for classifying data with categorical values. The batch learning rules make possible to construct the learning methodology for data in categorical nonvector spaces. Experiments on UCI data sets demonstrate the proposed algorithm is effective to improve the capability of standard LVQ to handle data with categorical values.

A bootstrapping algorithm for learning the polarity of words

Polarity lexicons are lists of words (or meanings) where each entry is labelled as positive, negative or neutral. These lists are not available for different languages and specific domains. This work proposes and evaluates a new algorithm to classify words as positive, negative or neutral, relying on a small seed set of words, a common dictionary and a propagation algorithm. We evaluate the positive and negative polarity propagation of words, as well as the neutral polarity. The propagation is evaluated with different settings and lexical resources.

The ubiquitous self-organizing map for non-stationary data streams

The Internet of things promises a continuous flow of data where traditional database and data-mining methods cannot be applied. This paper presents improvements on the Ubiquitous Self-Organized Map (UbiSOM), a novel variant of the well-known Self-Organized Map (SOM), tailored for streaming environments. This approach allows ambient intelligence solutions using multidimensional clustering over a continuous data stream to provide continuous exploratory data analysis. The average quantization error and average neuron utility over time are proposed and used to estimating the learning parameters, allowing the model to retain an indefinite plasticity and to cope with changes within a multidimensional data stream. We perform parameter sensitivity analysis and our experiments show that UbiSOM outperforms existing proposals in continuously modeling possibly non-stationary data streams, converging faster to stable models when the underlying distribution is stationary and reacting accordingly to the nature of the change in continuous real world data streams.

Automatic detection of meddies through texture analysis of sea surface temperature maps

A new machine learning approach is presented for automatic detection of Mediterranean water eddies from sea surface temperature maps of the Atlantic Ocean. A pre-processing step uses Laws’ convolution kernels to reveal microstructural patterns of water temperature. Given a map point, a numerical vector containing information on local structural properties is generated. This vector is forwarded to a multi-layer perceptron classifier that is trained to recognise texture patterns generated by positive and negative instances of eddy structures. The proposed system achieves high recognition accuracy with fast and robust learning results over a range of different combinations of statistical measures of texture properties. Detection results are characterised by a very low rate of false positives. The latter is particularly important since meddies occupy only a small portion of SST map area.

An Interactive Interface for Multi-dimensional Data Stream Analysis

Data mining models are frequently used to represent and summarize meaningful properties in data. However such models are usually not suitable for interactive data exploration and visualization. This paper proposes the use of multidimensional projection together with the Ubiquitous Self-Organizing Map algorithm (UbiSOM), a novel variant of the well-known self-organizing map algorithm that was specifically tuned for stream data-mining. The resulting high-dimensional projection system is then studied for interactive data analysis and visualization. A prototype was developed where, at each moment, the user can visualize the information from different perspectives. Direct interaction with the system during stream processing is possible both by changing the projection, by optimizing the projection view for maximizing variance or by filtering the incoming data series. Experiments in two distinct datasets show the importance and relevance of conjoining multidimensional data projection with UbiSOM.

Ubiquitous Self-Organizing Map: Learning Concept-Drifting Data Streams

The Internet of Things promises a continuous flow of data where traditional database and data-mining methods cannot be applied. This paper presents a novel variant of the well-known Self-Organized Map (SOM), called Ubiquitous SOM (UbiSOM), that is being tailored for streaming environments. This approach allows ambient intelligence solutions using multidimensional clustering over a continuous data stream to provide continuous exploratory data analysis. The average quantization error over time is used for estimating the learning parameters, allowing the model to retain an indefinite plasticity and to cope with concept drift within a multidimensional stream.