Miho Ohsaki

Interactive Evolutionary Computation-Based Hearing Aid Fitting

An interactive evolutionary computation (EC) fitting method is proposed that applies interactive EC to hearing aid fitting and the method is evaluated using a hearing aid simulator with human subjects. The advantages of the method are that it can optimize a hearing aid based on how a user hears and that it realizes whatever+whenever+wherever (W3) fitting. Conventional fitting methods are based on the users partially measured auditory characteristics, the fitting engineers experience, and the users linguistic explanation of his or her hearing. These conventional methods, therefore, suffer from the fundamental problem that no one can experience another persons hearing. However, as interactive EC fitting uses EC to optimize a hearing aid based on the users evaluation of his or her hearing, this problem is addressed. Moreover, whereas conventional fitting methods must use pure tones and bandpass noise for measuring hearing characteristics, our proposed method has no such restrictions. Evaluating the proposed method using speech sources, we demonstrate that it shows significantly better results than either the conventional method or the unprocessed case in terms of both speech intelligibility and speech quality. We also evaluate our method using musical sources, unusable for evaluation by conventional methods, and demonstrate that its sound quality is preferable to the unprocessed case

Evaluation of rule interestingness measures in medical knowledge discovery in databases

OBJECTIVE We discuss the usefulness of rule interestingness measures for medical KDD through experiments using clinical datasets, and, based on the outcomes of these experiments, also consider how to utilize these measures in postprocessing. METHODS AND MATERIALS We first conducted an experiment to compare the evaluation results derived from a total of 40 various interestingness measures with those supplied by a medical expert for rules discovered in a clinical dataset on meningitis. We calculated and compared the performance of each interestingness measure to estimate a medical experts interest using f-measure and correlation coefficient. We then conducted a similar experiment for hepatitis. RESULTS AND CONCLUSION The comprehensive results of experiments on meningitis and hepatitis indicate that the interestingness measures, accuracy, chi-square measure for one quadrant, relative risk, uncovered negative, and peculiarity, have a stable, reasonable performance in estimating real human interest in the medical domain. The results also indicate that the performance of interestingness measures is influenced by the certainty of a hypothesis made by the medical expert, and that the combinational use of interestingness measures will contribute to support medical experts to generate and confirm their hypotheses through human-system interaction.

Improvement of presenting interface by predicting the evaluation order to reduce the burden of human interactive EC operators

This paper proposes to display individuals of interactive evolutionary computations in an evaluation order to reduce the burden on human operators. To display an evaluation order, two prediction methods-the first using neural networks, and the second using Euclidean distance measure-are proposed. We evaluate their predictive performance through simulation experiments and subjective tests.

Implementing an integrated time-series data mining environment based on temporal pattern extraction methods: a case study of an interferon therapy risk mining for chronic hepatitis

In this paper, we present the implementation of an integrated time-series data mining environment. Time-series data mining is one of key issues to get useful knowledge from databases. With mined time-series patterns, users can aware not only positive results but also negative result called risk after their observation period. However, users often face difficulties during time-series data mining process for data pre-processing method selection/construction, mining algorithm selection, and post-processing to refine the data mining process as other data mining processes. It is needed to develop a time-series data mining environment based on systematic analysis of the process. To get more valuable rules for domain experts from a time-series data mining process, we have designed an environment which integrates time-series pattern extraction methods, rule induction methods and rule evaluation methods with active human-system interaction. After implementing this environment, we have done a case study to mine time-series rules from blood and urine biochemical test database on chronic hepatitis patients. Then a physician has evaluated and refined his hypothesis on this environment. We discuss the availability of how much support to mine interesting knowledge for an expert.

IEC-based hearing aid fitting

We propose a hearing aid fitting method based on an interactive evolutionary computation (IEC). First, we identify the problems with current hearing aid fitting methods and propose an IEC fitting method to improve the fitting process with completely different approach from that of conventional fitting methods. Then, we design an experimental hearing aid system and evaluate the IEC fitting method. Using speech and music sources, we show that our proposed IEC fitting method outperforms conventional ones.

Comparison between objective interestingness measures and real human interest in medical data mining

One of the difficulties of using Artificial Neural Networks (ANNs) to estimate atmospheric temperature is the large number of potential input variables available. In this study, four different feature extraction methods were used to reduce the input vector to train four networks to estimate temperature at different atmospheric levels. The four techniques used were: genetic algorithms (GA), coefficient of determination (CoD), mutual information (MI) and simple neural analysis (SNA). The results demonstrate that of the four methods used for this data set, mutual information and simple neural analysis can generate networks that have a smaller input parameter set, while still maintaining a high degree of accuracy.

A rule evaluation support method with learning models based on objective rule evaluation indexes

In this paper, we present a novel rule evaluation support method for post-processing of mined results with rule evaluation models based on objective indexes. Post-processing of mined results is one of the key issues to make a data mining process successfully. However, it is difficult for human experts to evaluate many thousands of rules from a large dataset with noises completely. To reduce the costs of rule evaluation procedures, we have developed the rule evaluation support method with rule evaluation models, which are obtained with objective rule evaluation indexes and evaluations of a human expert for each rule. Since the method is needed more accurate rule evaluation models, we have compared learning algorithms to construct rule evaluation models with the actual meningitis data mining result and actual rule sets from UCI datasets. Then we show the availability of our adaptive rule evaluation support method.

Developing an Integrated Time-Series Data Mining Environment for Medical Data Mining

In this paper, we present an integrated time-series data mining environment for medical data mining. Medical time-series data mining is one of key issues to get useful clinical knowledge from medical databases. However, users often face difficulties during such medical time-series data mining process for data preprocessing method selection/construction, mining algorithm selection, and post-processing to refine the data mining process as shown in other data mining processes. To get more valuable rules for medical experts from a time-series data mining process, we have designed an environment which integrates time- series pattern extraction methods, rule induction methods and rule evaluation methods with visual human-system interface. After implementing this environment, we have done a case study to mine time- series rules from blood/urine biochemical test database on chronic hepatitis patients. The result shows the availability to find out valuable clinical course rules based on time-series pattern extraction. Furthermore, we compared the difference of time-series pattern extraction methods with objective rule evaluation results.

Investigation of rule interestingness in medical data mining

This research experimentally investigates the performance of conventional rule interestingness measures and discusses their usefulness for supporting KDD through human-system interaction in medical domain. We compared the evaluation results by a medical expert and those by selected sixteen kinds of interestingness measures for the rules discovered in a dataset on hepatitis. X 2 measure, recall, and accuracy demonstrated the highest performance, and specificity and prevalence did the lowest. The interestingness measures showed a complementary relationship for each other. These results indicated that some interestingness measures have the possibility to predict really interesting rules at a certain level and that the combinational use of interestingness measures will be useful. We then discussed how to combinationally utilize interestingness measures and proposed a post-processing user interface utilizing them, which supports KDD through human-system interaction.

Minimum Error Classification with geometric margin control

Minimum Classification Error (MCE) training, which can be used to achieve minimum error classification of various types of patterns, has attracted a great deal of attention. However, to increase classification robustness, a conventional MCE framework has no practical optimization procedures like geometric margin maximization in Support Vector Machine (SVM). To realize high robustness in a wide range of classification tasks, we derive the geometric margin for a general class of discriminant functions and develop a new MCE training method that increases the geometric margin value. We also experimentally demonstrate the effectiveness of our new method using prototype-based classifiers.