Dada a grande quantidade de dados gerados atualmente, apenas uma pequena porção dos mesmos pode ser rotulada manualmente por especialistas humanos. Isso é um desafio comum para aplicações de aprendizagem de máquina. Aprendizado semi-supervisionado aborda este problema através da manipulação dos dados não rotulados juntamente aos dados rotulados. Entretanto, se apenas uma quantidade limitada de exemplos rotulados está disponível, o desempenho da tarefa de aprendizagem de máquina (e.g., classificação) pode ser não satisfatória. Diversas soluções abordam este problema através do uso de uma ensemble de classificadores, visto que essa abordagem aumenta a diversidade dos classificadores. Algoritmos como o co-training e o tri-training utilizam múltiplas partições de dados ou múltiplos algoritmos de aprendizado para melhorar a qualidade da classificação de instâncias não rotuladas através de concordância por maioria simples. Além disso, existem abordagens que estendem esta ideia e adotam processos de votação menos triviais para definir os rótulos, como eleição por maioria ponderada, por exemplo. Contudo, estas soluções requerem que os rótulos possuam um certo nível de confiança para serem utilizados no treinamento. Consequentemente, nem toda a informação disponível é utilizada. Por exemplo: informações associadas a níveis de confiança baixos são totalmente ignoradas. Este trabalho propõe uma abordagem chamada social-training, que utiliza toda a informação disponível na tarefa de aprendizado semi-supervisionado. Para isto, múltiplos classificadores heterogêneos são treinados com os dados rotulados e geram diversas classificações para as mesmas instâncias não rotuladas. O social-training, então, agrega estes resultados em um único rótulo por meio de funções de escolha social que trabalham com agregação de rankings sobre as instâncias. Especificamente, a solução trabalha com casos de classificação binária. Os resultados mostram que trabalhar com o ranking completo, ou seja, rotular todas as instâncias não rotuladas, é capaz de reduzir o erro de classificação para alguns conjuntos de dados da base da UCI utilizados. / Given the huge quantity of data currently being generated, just a small portion of it can be manually labeled by human experts. This is a challenge for machine learning applications. Semi-supervised learning addresses this problem by handling unlabeled data alongside labeled ones. However, if only a limited quantity of labeled examples is available, the performance of the machine learning task (e.g., classification) can be very unsatisfactory. Many solutions address this issue by using a classifier ensemble because this increases diversity. Algorithms such as co-training and tri-training use multiple views or multiple learning algorithms in order to improve the classification of unlabeled instances through simple majority agreement. Also, there are approaches that extend this idea and adopt less trivial voting processes to define the labels, like weighted majority voting. Nevertheless, these solutions require some confidence level on the label in order to use it for training. Hence, not all information is used, i.e., information associated with low confidence level is disregarded completely. An approach called social-training is proposed, which uses all information available in the semi-supervised learning task. For this, multiple heterogeneous classifiers are trained with the labeled data and generate diverse classifications for the same unlabeled instances. Social-training then aggregates these results into a single label by means of social choice functions that work with rank aggregation over the instances. The solution addresses binary classification cases. The results show that working with the full ranking, i.e., labeling all unlabeled instances, is able to reduce the classification error for some UCI data sets used.

Dada a grande quantidade de dados gerados atualmente, apenas uma pequena porção dos mesmos pode ser rotulada manualmente por especialistas humanos. Isso é um desafio comum para aplicações de aprendizagem de máquina. Aprendizado semi-supervisionado aborda este problema através da manipulação dos dados não rotulados juntamente aos dados rotulados. Entretanto, se apenas uma quantidade limitada de exemplos rotulados está disponível, o desempenho da tarefa de aprendizagem de máquina (e.g., classificação) pode ser não satisfatória. Diversas soluções abordam este problema através do uso de uma ensemble de classificadores, visto que essa abordagem aumenta a diversidade dos classificadores. Algoritmos como o co-training e o tri-training utilizam múltiplas partições de dados ou múltiplos algoritmos de aprendizado para melhorar a qualidade da classificação de instâncias não rotuladas através de concordância por maioria simples. Além disso, existem abordagens que estendem esta ideia e adotam processos de votação menos triviais para definir os rótulos, como eleição por maioria ponderada, por exemplo. Contudo, estas soluções requerem que os rótulos possuam um certo nível de confiança para serem utilizados no treinamento. Consequentemente, nem toda a informação disponível é utilizada. Por exemplo: informações associadas a níveis de confiança baixos são totalmente ignoradas. Este trabalho propõe uma abordagem chamada social-training, que utiliza toda a informação disponível na tarefa de aprendizado semi-supervisionado. Para isto, múltiplos classificadores heterogêneos são treinados com os dados rotulados e geram diversas classificações para as mesmas instâncias não rotuladas. O social-training, então, agrega estes resultados em um único rótulo por meio de funções de escolha social que trabalham com agregação de rankings sobre as instâncias. Especificamente, a solução trabalha com casos de classificação binária. Os resultados mostram que trabalhar com o ranking completo, ou seja, rotular todas as instâncias não rotuladas, é capaz de reduzir o erro de classificação para alguns conjuntos de dados da base da UCI utilizados. / Given the huge quantity of data currently being generated, just a small portion of it can be manually labeled by human experts. This is a challenge for machine learning applications. Semi-supervised learning addresses this problem by handling unlabeled data alongside labeled ones. However, if only a limited quantity of labeled examples is available, the performance of the machine learning task (e.g., classification) can be very unsatisfactory. Many solutions address this issue by using a classifier ensemble because this increases diversity. Algorithms such as co-training and tri-training use multiple views or multiple learning algorithms in order to improve the classification of unlabeled instances through simple majority agreement. Also, there are approaches that extend this idea and adopt less trivial voting processes to define the labels, like weighted majority voting. Nevertheless, these solutions require some confidence level on the label in order to use it for training. Hence, not all information is used, i.e., information associated with low confidence level is disregarded completely. An approach called social-training is proposed, which uses all information available in the semi-supervised learning task. For this, multiple heterogeneous classifiers are trained with the labeled data and generate diverse classifications for the same unlabeled instances. Social-training then aggregates these results into a single label by means of social choice functions that work with rank aggregation over the instances. The solution addresses binary classification cases. The results show that working with the full ranking, i.e., labeling all unlabeled instances, is able to reduce the classification error for some UCI data sets used.

Dada a grande quantidade de dados gerados atualmente, apenas uma pequena porção dos mesmos pode ser rotulada manualmente por especialistas humanos. Isso é um desafio comum para aplicações de aprendizagem de máquina. Aprendizado semi-supervisionado aborda este problema através da manipulação dos dados não rotulados juntamente aos dados rotulados. Entretanto, se apenas uma quantidade limitada de exemplos rotulados está disponível, o desempenho da tarefa de aprendizagem de máquina (e.g., classificação) pode ser não satisfatória. Diversas soluções abordam este problema através do uso de uma ensemble de classificadores, visto que essa abordagem aumenta a diversidade dos classificadores. Algoritmos como o co-training e o tri-training utilizam múltiplas partições de dados ou múltiplos algoritmos de aprendizado para melhorar a qualidade da classificação de instâncias não rotuladas através de concordância por maioria simples. Além disso, existem abordagens que estendem esta ideia e adotam processos de votação menos triviais para definir os rótulos, como eleição por maioria ponderada, por exemplo. Contudo, estas soluções requerem que os rótulos possuam um certo nível de confiança para serem utilizados no treinamento. Consequentemente, nem toda a informação disponível é utilizada. Por exemplo: informações associadas a níveis de confiança baixos são totalmente ignoradas. Este trabalho propõe uma abordagem chamada social-training, que utiliza toda a informação disponível na tarefa de aprendizado semi-supervisionado. Para isto, múltiplos classificadores heterogêneos são treinados com os dados rotulados e geram diversas classificações para as mesmas instâncias não rotuladas. O social-training, então, agrega estes resultados em um único rótulo por meio de funções de escolha social que trabalham com agregação de rankings sobre as instâncias. Especificamente, a solução trabalha com casos de classificação binária. Os resultados mostram que trabalhar com o ranking completo, ou seja, rotular todas as instâncias não rotuladas, é capaz de reduzir o erro de classificação para alguns conjuntos de dados da base da UCI utilizados. / Given the huge quantity of data currently being generated, just a small portion of it can be manually labeled by human experts. This is a challenge for machine learning applications. Semi-supervised learning addresses this problem by handling unlabeled data alongside labeled ones. However, if only a limited quantity of labeled examples is available, the performance of the machine learning task (e.g., classification) can be very unsatisfactory. Many solutions address this issue by using a classifier ensemble because this increases diversity. Algorithms such as co-training and tri-training use multiple views or multiple learning algorithms in order to improve the classification of unlabeled instances through simple majority agreement. Also, there are approaches that extend this idea and adopt less trivial voting processes to define the labels, like weighted majority voting. Nevertheless, these solutions require some confidence level on the label in order to use it for training. Hence, not all information is used, i.e., information associated with low confidence level is disregarded completely. An approach called social-training is proposed, which uses all information available in the semi-supervised learning task. For this, multiple heterogeneous classifiers are trained with the labeled data and generate diverse classifications for the same unlabeled instances. Social-training then aggregates these results into a single label by means of social choice functions that work with rank aggregation over the instances. The solution addresses binary classification cases. The results show that working with the full ranking, i.e., labeling all unlabeled instances, is able to reduce the classification error for some UCI data sets used.

EMG signal decomposition is the process of resolving a composite EMG signal into its constituent motor unit potential trains (classes) and it can be configured as a classification problem. An EMG signal detected by the tip of an inserted needle electrode is the superposition of the individual electrical contributions of the different motor units that are active, during a muscle contraction, and background interference. <BR>This thesis addresses the process of EMG signal decomposition by developing an interactive classification system, which uses multiple classifier fusion techniques in order to achieve improved classification performance. The developed system combines heterogeneous sets of base classifier ensembles of different kinds and employs either a one level classifier fusion scheme or a hybrid classifier fusion approach. <BR>The hybrid classifier fusion approach is applied as a two-stage combination process that uses a new aggregator module which consists of two combiners: the first at the abstract level of classifier fusion and the other at the measurement level of classifier fusion such that it uses both combiners in a complementary manner. Both combiners may be either data independent or the first combiner data independent and the second data dependent. For the purpose of experimentation, we used as first combiner the majority voting scheme, while we used as the second combiner one of the fixed combination rules behaving as a data independent combiner or the fuzzy integral with the lambda-fuzzy measure as an implicit data dependent combiner. <BR>Once the set of motor unit potential trains are generated by the classifier fusion system, the firing pattern consistency statistics for each train are calculated to detect classification errors in an adaptive fashion. This firing pattern analysis allows the algorithm to modify the threshold of assertion required for assignment of a motor unit potential classification individually for each train based on an expectation of erroneous assignments. <BR>The classifier ensembles consist of a set of different versions of the Certainty classifier, a set of classifiers based on the nearest neighbour decision rule: the fuzzy <em>k</em>-NN and the adaptive fuzzy <em>k</em>-NN classifiers, and a set of classifiers that use a correlation measure as an estimation of the degree of similarity between a pattern and a class template: the matched template filter classifiers and its adaptive counterpart. The base classifiers, besides being of different kinds, utilize different types of features and their performances were investigated using both real and simulated EMG signals of different complexities. The feature sets extracted include time-domain data, first- and second-order discrete derivative data, and wavelet-domain data. <BR>Following the so-called <em>overproduce and choose</em> strategy to classifier ensemble combination, the developed system allows the construction of a large set of candidate base classifiers and then chooses, from the base classifiers pool, subsets of specified number of classifiers to form candidate classifier ensembles. The system then selects the classifier ensemble having the maximum degree of agreement by exploiting a diversity measure for designing classifier teams. The kappa statistic is used as the diversity measure to estimate the level of agreement between the base classifier outputs, i. e. , to measure the degree of decision similarity between the base classifiers. This mechanism of choosing the team's classifiers based on assessing the classifier agreement throughout all the trains and the unassigned category is applied during the one level classifier fusion scheme and the first combiner in the hybrid classifier fusion approach. For the second combiner in the hybrid classifier fusion approach, we choose team classifiers also based on kappa statistics but by assessing the classifiers agreement only across the unassigned category and choose those base classifiers having the minimum agreement. <BR>Performance of the developed classifier fusion system, in both of its variants, i. e. , the one level scheme and the hybrid approach was evaluated using synthetic simulated signals of known properties and real signals and then compared it with the performance of the constituent base classifiers. Across the EMG signal data sets used, the hybrid approach had better average classification performance overall, specially in terms of reducing the number of classification errors.

EMG signal decomposition is the process of resolving a composite EMG signal into its constituent motor unit potential trains (classes) and it can be configured as a classification problem. An EMG signal detected by the tip of an inserted needle electrode is the superposition of the individual electrical contributions of the different motor units that are active, during a muscle contraction, and background interference. <BR>This thesis addresses the process of EMG signal decomposition by developing an interactive classification system, which uses multiple classifier fusion techniques in order to achieve improved classification performance. The developed system combines heterogeneous sets of base classifier ensembles of different kinds and employs either a one level classifier fusion scheme or a hybrid classifier fusion approach. <BR>The hybrid classifier fusion approach is applied as a two-stage combination process that uses a new aggregator module which consists of two combiners: the first at the abstract level of classifier fusion and the other at the measurement level of classifier fusion such that it uses both combiners in a complementary manner. Both combiners may be either data independent or the first combiner data independent and the second data dependent. For the purpose of experimentation, we used as first combiner the majority voting scheme, while we used as the second combiner one of the fixed combination rules behaving as a data independent combiner or the fuzzy integral with the lambda-fuzzy measure as an implicit data dependent combiner. <BR>Once the set of motor unit potential trains are generated by the classifier fusion system, the firing pattern consistency statistics for each train are calculated to detect classification errors in an adaptive fashion. This firing pattern analysis allows the algorithm to modify the threshold of assertion required for assignment of a motor unit potential classification individually for each train based on an expectation of erroneous assignments. <BR>The classifier ensembles consist of a set of different versions of the Certainty classifier, a set of classifiers based on the nearest neighbour decision rule: the fuzzy <em>k</em>-NN and the adaptive fuzzy <em>k</em>-NN classifiers, and a set of classifiers that use a correlation measure as an estimation of the degree of similarity between a pattern and a class template: the matched template filter classifiers and its adaptive counterpart. The base classifiers, besides being of different kinds, utilize different types of features and their performances were investigated using both real and simulated EMG signals of different complexities. The feature sets extracted include time-domain data, first- and second-order discrete derivative data, and wavelet-domain data. <BR>Following the so-called <em>overproduce and choose</em> strategy to classifier ensemble combination, the developed system allows the construction of a large set of candidate base classifiers and then chooses, from the base classifiers pool, subsets of specified number of classifiers to form candidate classifier ensembles. The system then selects the classifier ensemble having the maximum degree of agreement by exploiting a diversity measure for designing classifier teams. The kappa statistic is used as the diversity measure to estimate the level of agreement between the base classifier outputs, i. e. , to measure the degree of decision similarity between the base classifiers. This mechanism of choosing the team's classifiers based on assessing the classifier agreement throughout all the trains and the unassigned category is applied during the one level classifier fusion scheme and the first combiner in the hybrid classifier fusion approach. For the second combiner in the hybrid classifier fusion approach, we choose team classifiers also based on kappa statistics but by assessing the classifiers agreement only across the unassigned category and choose those base classifiers having the minimum agreement. <BR>Performance of the developed classifier fusion system, in both of its variants, i. e. , the one level scheme and the hybrid approach was evaluated using synthetic simulated signals of known properties and real signals and then compared it with the performance of the constituent base classifiers. Across the EMG signal data sets used, the hybrid approach had better average classification performance overall, specially in terms of reducing the number of classification errors.