Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression
The problem of learning with positive and unlabeled examples arises frequently in retrieval applications. We transform the problem into a problem of learning with noise by labeling all unlabeled examples as negative and use a linear function to learn from the noisy examples. To learn a linear function with noise, we perform logistic regression after weighting the examples to handle noise rates of greater than a half. With appropriate regularization, the cost function of the logistic regression problem is convex, allowing the problem to be solved efficiently. We also propose a performance measure that can be estimated from positive and unlabeled examples for evaluating retrieval performance. The measure, which is proportional to the product of precision and recall, can be used with a validation set to select regularization parameters for logistic regression. Experiments on a text classification corpus show that the methods proposed are effective.

title: Opinion Mining
abstract: Описанл процедуру формування інтегрального показника об’єкта згідно з відгуками користувачів під час застосування методів оцінювання опінії текстової інформації у web-документах. Запропоновано використання лінгвістичних змінних та застосування вагових коефіцієнтів для достовірнішого результату оцінювання емоційного забарвлення текстової інформації. Ключові слова: опінія, емоційне забарвлення, об’єкт, інтегральний показник, лінгвістична змінна, ваговий коефіцієнт.

title: Finding Actionable Knowledge via Automated Comparison
abstract: The problem of finding interesting and actionable patterns is a major challenge in data mining. It has been studied by many data mining researchers. The issue is that data mining algorithms often generate too many patterns, which make it very hard for the user to find those truly useful ones. Over the years many techniques have been proposed. However, few have made it to real-life applications. At the end of 2005, we built a data mining system for Motorola (called Opportunity Map) to enable the user to explore the space of a large number of rules in order to find actionable knowledge. The approach is based on the concept of rule cubes and operations on rule cubes. A rule cube is similar to a data cube, but stores rules. Since its deployment, some issues have also been identified during the regular use of the system in Motorola. One of the key issues is that although the operations on rule cubes are flexible, each operation is primitive and has to be initiated by the user. Finding a piece of actionable knowledge typically involves many operations and intense visual inspections, which are labor-intensive and time-consuming. From interactions with our users, we identified a generic problem that is crucial for finding actionable knowledge. The problem involves extensive comparison of sub-populations and identification of the cause of their differences. This paper first defines the problem and then proposes an effective method to solve the problem automatically. To the best of our knowledge, there is no reported study of this problem. The new method has been added to the Opportunity Map system and is now in daily use in Motorola.

title: Speed-up iterative frequent itemset mining with constraint changes
abstract: Mining of frequent itemsets is a fundamental data mining task. Past research has proposed many efficient algorithms for this purpose. Recent work also highlighted the importance of using constraints to focus the mining process to mine only those relevant itemsets. In practice, data mining is often an interactive and iterative process. The user typically changes constraints and runs the mining algorithm many times before being satisfied with the final results. This interactive process is very time consuming. Existing mining algorithms are unable to take advantage of this iterative process to use previous mining results to speed up the current mining process. This results in an enormous waste of time and computation. In this paper, we propose an efficient technique to utilize previous mining results to improve the efficiency of current mining when constraints are changed. We first introduce the concept of tree boundary to summarize useful information available from previous mining. We then show that the tree boundary provides an effective and efficient framework for the new mining. The proposed technique has been implemented in the context of two existing frequent itemset mining algorithms, FP-tree and tree projection. Experiment results on both synthetic and real-life datasets show that the proposed approach achieves a dramatic saving of computation.

title: Entity discovery and assignment for opinion mining applications
abstract: Opinion mining became an important topic of study in recent years due to its wide range of applications. There are also many companies offering opinion mining services. One problem that has not been studied so far is the assignment of entities that have been talked about in each sentence. Let us use forum discussions about products as an example to make the problem concrete. In a typical discussion post, the author may give opinions on multiple products and also compare them. The issue is how to detect what products have been talked about in each sentence. If the sentence contains the product names, they need to be identified. We call this problem entity discovery. If the product names are not explicitly mentioned in the sentence but are implied due to the use of pronouns and language conventions, we need to infer the products. We call this problem entity assignment. These problems are important because without knowing what products each sentence talks about the opinion mined from the sentence is of little use. In this paper, we study these problems and propose two effective methods to solve the problems. Entity discovery is based on pattern discovery and entity assignment is based on mining of comparative sentences. Experimental results using a large number of forum posts demonstrate the effectiveness of the technique. Our system has also been successfully tested in a commercial setting.

title: Dealing with different distributions in learning from
abstract: In the problem of learning with positive and unlabeled examples, existing research all assumes that positive examples P and the hidden positive examples in the unlabeled set U are generated from the same distribution. This assumption may be violated in practice. In such cases, existing methods perform poorly. This paper proposes a novel technique A-EM to deal with the problem. Experimental results with product page classification demonstrate the effectiveness of the proposed technique.

title: A Novel Fuzzy Neural Network with Fast Training and Accurate Generalization
abstract: For the reason of all parameters in a conventional fuzzy neural network (FNN) needed to be adjusted iteratively, learning can be very slow and may suffer from local minima. To overcome these problems, we propose a novel FNN in this paper, which shows a fast speed and accurate generalization. First we state the universal approximation theorem for an FNN with random membership function parameters (FNN-RM). Since all the membership function parameters are arbitrarily chosen, the proposed FNN-RM algorithm needs to adjust only the output weights of FNNs. Experimental results on function approximation and classification problems show that the new algorithm not only provides thousands of times of speed-up over traditional learning algorithms, but also produces better generalization performance in comparison to other FNNs.

title: A Quality Metric Based on Color Similarity for Image Fusion
abstract: This paper proposes a new objective, non-reference quality metric for evaluating the performance of pixel-level image fusion. The metric is designed on a likelihood function for color cue that measures the similarity of color- or grey-level distributions. We have provided a theoretical analysis on how the quality metric responds to weighted averaging fusion algorithm. Then the slide window is used to calculate the metric over local region with the weight which is assessed by local saliency and indicates the relative importance. Extensive experiments have demonstrated that the metric is more effective and consistent with subjective evaluations compared with other metrics.

title: Support Vector Machines for Text Categorization in Chinese Question Classification
abstract: Question classification plays a crucial important role in the question answering system because categorizing a given question is beneficial to identify an answer in the documents. The goal of question classification is to accurately assign labels to question based on expected answer type. Recently, many machine learning algorithms are used for question classification. However many research results show that SVM perform best in this task, because it is well known to work well for nonlinear, sparse, high dimensional problems. In this experiment, we perform the One-against-One SVM algorithm and a feature extraction method of Chinese questions to get high classification accuracy

title: Nesting One-Against-One Algorithm Based on SVMs for Pattern Classification
abstract: Support vector machines (SVMs), which were originally designed for binary classifications, are an excellent tool for machine learning. For the multiclass classifications, they are usually converted into binary ones before they can be used to classify the examples. In the one-against-one algorithm with SVMs, there exists an unclassifiable region where the data samples cannot be classified by its decision function. This paper extends the one-against-one algorithm to handle this problem. We also give the convergence and computational complexity analysis of the proposed method. Finally, one-against-one, fuzzy decision function (FDF), and decision-directed acyclic graph (DDAG) algorithms and our proposed method are compared using five University of California at Irvine (UCI) data sets. The results report that the proposed method can handle the unclassifiable region better than others.

title: A Novel RBF Neural Network with Fast Training and Accurate Generalization
abstract: For the reason of all the centers and radii needed to be adjusted iteratively, the learning speed of radial basis function (RBF) neural networks is always far slower than required, which obviously forms a bottleneck in many applications. To overcome such problem, we propose a fast and accurate RBF neural network in this paper. First we prove the universal approximation theorem for RBF neural networks with arbitrary centers and radii. Based on this theory, we propose a new learning algorithm called fast and accurate RBF neural network with random kernels (RBF-RK). With the arbitrary centers and radii, our RBF-RK algorithm only needs to adjust the output weights. The experimental results, on function approximation and classification problems, show that the new algorithm not only runs much faster than traditional learning algorithms, but also produces better or at least comparable generalization performance.