Roman B. Sergienko

Competitive cooperation for strategy adaptation in coevolutionary genetic algorithm for constrained optimization

A coevolutionary algorithm as a search strategy adaptation procedure in constrained optimization is discussed in the paper. The coevolutionary algorithm consists of the set of individual conventional genetic algorithms with different search strategies. Individual genetic algorithms compete and cooperate with each other. Competition is provided with resource re-allocation among algorithms and cooperation is provided with migration of the best individuals to all of the algorithms. At early works this method was applied for unconstrained optimization problems. The common result was that coevolutionary algorithm is more effective than average individual genetic algorithms. In this paper modification of competitive-cooperative coevolutionary algorithm for constrained optimization problems is considered. Results of test comparison of coevolutionary algorithm with conventional genetic algorithms demonstrate that coevolutionary algorithm is not less effective than the best for problem-in-hand individual conventional algorithm.

Opinion Mining and Topic Categorization with Novel Term Weighting

In this paper we investigate the efficiency of the novel term weighting algorithm for opinion mining and topic categorization of articles from newspapers and Internet. We compare the novel term weighting technique with existing approaches such as TF-IDF and ConfWeight. The performance on the data from the text-mining campaigns DEFT’07 and DEFT’08 shows that the proposed method can compete with existing information retrieval models in classification quality and that it is computationally faster. The proposed text preprocessing method can be applied in large-scale information retrieval and data mining problems and it can be easily transported to different domains and different languages since it does not require any domain-related or linguistic information.

First Experiments on Interaction Quality Modelling for Human-Human Conversation

This work presents the first experimental results on Interaction Quality modelling for human-human conversation, as an adaptation of the Interaction Quality metric for human-computer spoken interaction. The prediction of an Interaction Quality score can be formulated as a classification problem. In this paper we describe the results of applying several classification algorithms such as: Kernel Naive Bayes Classifier, k-Nearest Neighbours algorithm, Logistic Regression, and Support Vector Machines, to a data set. Moreover, we compare the results of modelling for two approaches for Interaction Quality labelling and consider the results depending on different emotion sets. The results of Interaction Quality modelling for human-human conversation may be used both for improving the service quality in call centres and for improving Spoken Dialogue Systems in terms of flexibility, user-friendliness and human-likeness.

An Approach to Off-talk Detection based on Text Classification within an Automatic Spoken Dialogue System

This paper describes the problem of the off-talk detection within an automatic spoken dialogue system. The considered corpus contains realistic conversations between two users and an SDS. A two- (on-talk and off-talk) and a three-class (on-talk, problem-related off-talk, and irrelevant off-talk) problem statement are investigated using a speaker-independent approach to cross-validation. A novel off-talk detection approach based on text classification is proposed. Seven different term weighting methods and two classification algorithms are considered. As a dimensionality reduction method, a feature transformation based on term belonging to classes is applied. The comparative analysis of the proposed approach and a baseline one is performed; as a result, the best combinations of the text pre-processing methods and classification algorithms are defined for both problem statements. The novel approach demonstrates significantly better classification effectiveness in comparison with the baseline for the same task.

Dimension reduction with coevolutionary genetic algorithm for text classification

Text classification of large-size corpora is time-consuming for implementation of classification algorithms. For this reason, it is important to reduce dimension of text classification problems. We propose a method for dimension reduction based on hierarchical agglomerative clustering of terms and cluster weight optimization using cooperative coevolutionary genetic algorithm. The method was applied on 5 different corpora using several classification methods with different text preprocessing. The method reduces dimension of text classification problem significantly. Classification efficiency increases or decreases non-significantly after clustering with optimization of cluster weights.

Text categorization methods application for natural language call routing

Natural language call routing can be treated as an instance of topic categorization of documents after speech recognition of calls. This categorization consists of two important parts. The first one is text preprocessing for numerical data extraction and the second one is classification with machine learning methods. This paper focuses on different text preprocessing methods applied for call routing. Different machine learning algorithms with several text representations have been applied for this problem. A novel text preprocessing technique has been applied and investigated. Numerical experiments have shown computational and classification effectiveness of the proposed method in comparison with standard techniques. Also a novel features selection method was proposed. The novel features selection method has demonstrated some advantages in comparison with standard techniques.

A Comparative Study of Text Preprocessing Techniques for Natural Language Call Routing

The article describes a comparative study of text preprocessing techniques for natural language call routing. Seven different unsupervised and supervised term weighting methods were considered. Four different dimensionality reduction methods were applied: stop-words filtering with stemming, feature selection based on term weights, feature transformation based on term clustering, and a novel feature transformation method based on terms belonging to classes. As classification algorithms we used k-NN and the SVM-based algorithm Fast Large Margin. The numerical experiments showed that the most effective term weighting method is Term Relevance Ratio (TRR). Feature transformation based on term clustering is able to significantly decrease dimensionality without significantly changing the classification effectiveness, unlike other dimensionality reduction methods. The novel feature transformation method reduces the dimensionality radically: number of features is equal to number of classes.

Collectives of Term Weighting Methods for Natural Language Call Routing

The paper presents the investigation of collectives of term weighting methods for natural language call routing. The database consists of user utterances recorded in English language from caller interactions with commercial automated agents. Utterances from this database are labelled by experts and divided into 20 classes. Seven different unsupervised and supervised term weighting methods were tested and compared with each other for classification with k-NN. Also a novel feature extraction method based on terms belonging to classes was applied. After that different combinations of term weighting methods were formed as collectives and used for meta-classification with rule induction. The numerical experiments have shown that the combination of two best term weighting methods (Term Relevance Ratio and Confident Weights) increases classification effectiveness in comparison with the best individual term weighting method significantly.

Multistep fuzzy classifier forming with cooperative-competitive coevolutionary algorithm

This paper is about multistep fuzzy classifier forming method with cooperative-competitive coevolutionary algorithm. Cooperative-competitive coevolutionary algorithm automatically allows avoiding the problem of genetic algorithm parameters setting. This approach is included in a new method combining Michigan and Pittsburgh approaches for fuzzy classifier design. The procedure is performed several times. After each step classification efficiency is increased and standard deviation of values is decreased. Results of numerical experiments for machine learning problems from UCI repository are presented.

Weighted Voting of Different Term Weighting Methods for Natural Language Call Routing

The text classification problem for natural language call routing was considered in the paper. Seven different term weighting methods were applied. As dimensionality reduction methods, the combination of stop-word filtering and stemming and the feature transformation based on term belonging to classes were considered. kNN and SVM-FML were used as classification algorithms. In the paper the idea of voting with different term weighting methods was proposed. The majority vote of seven considered term weighting methods provides significant improvement of classification effectiveness. After that the weighted voting based on optimization with self-adjusting genetic algorithm was investigated. The numerical results showed that weighted voting provides additional improvement of classification effectiveness. Especially significant improvement of the classification effectiveness is observed with the feature transformation based on term belonging to classes that reduces the dimensionality radically; the dimensionality equals number of classes. Therefore, it can be useful for real-time systems as natural language call routing.