Makoto Suzuki

Text categorization based on the ratio of word frequency in each categories

In the present paper, we consider the automatic text categorization as a series of information processing and propose a new classification technique called the Frequency Ratio Accumulation Method (FRAM). This is a simple technique that calculates the sum of ratios of word frequency in each category. However, in FRAM, feature terms can be used without limit. Therefore, we propose the use of the character N-gram and the word N-gram as feature terms using the above-described property of FRAM. Next, we evaluate the proposed technique through a number of experiments. In these experiments, we classify newspaper articles from Japanese CD-Mainichi 2002 and English Reuters-21578 using the Naive Bayes method (baseline method) and the proposed method. As a result, we show that the classification accuracy of the proposed method is far better than that of the baseline method. Specifically, the classification accuracy of the proposed method is 87.3% for Japanese CD-Mainichi 2002 and 86.1% for English Reuters-21578. Thus, the proposed method has very high performance. Although the proposed method is a simple technique, it provides a new perspective and has a high potential and is language-independent. Thus, the proposed method can be expected to be developed further in the future.

On a new model for automatic text categorization based on Vector Space Model

In our previous paper, we proposed a new classification technique called the Frequency Ratio Accumulation Method (FRAM). This is a simple technique that adds up the ratios of term frequencies among categories, and it is able to use index terms without limit. Then, we adopted the Character N-gram to form index terms, thereby improving FRAM. However, FRAM did not have a satisfactory mathematical basis. Therefore, we present here a new mathematical model based on a “Vector Space Model” and consider its implications. The proposed method is evaluated by performing several experiments. In these experiments, we classify newspaper articles from the English Reuters-21578 data set, a Japanese CD-Mainichi 2002 data set using the proposed method. The Reuters-21578 data set is a benchmark data set for automatic text categorization. It is shown that FRAM has good classification accuracy. Specifically, the micro-averaged F-measure of the proposed method is 92.2% for English. The proposed method can perform classification utilizing a single program and it is language-independent.

English and Taiwanese text categorization using N-gram based on Vector Space Model

In this paper, we present a new mathematical model based on a “Vector Space Model” and consider its implications. The proposed method is evaluated by performing several experiments. In these experiments, we classify newspaper articles from the English Reuters-21578 data set, and Taiwanese China Times 2005 data set using the proposed method. The Reuters-21578 data set is a benchmark data set for automatic text categorization. It is shown that FRAM has good classification accuracy. Specifically, the micro-averaged F-measure of the proposed method is 94.5% for English. However, that is 78.0% for Taiwanese. Though the proposed method is language-independent and provides a new perspective, our future work is to improve classification accuracy for Taiwanese.

Multilingual text categorization using Character N-gram

In our previous paper, we proposed a new classification technique called the Frequency Ratio Accumulation Method (FRAM). This is a simple technique that adds up the ratios of term frequency among categories. However, in FRAM, the use of feature terms is unlimited. In the present paper, we adopt character N-gram as feature terms improving the above-described particularity of FRAM. That is to say, the proposed method is language-independent because it does not depend on the low of grammar by using character N-gram. Therefore, we can classify multi-language into some categories using only one program. Next, the proposed method is evaluated by performing several experiments. In these experiments, we classify newspaper articles from English Reuters-21578, Japanese CD-Mainichi 2002 and Chinese China Times 2005 using FRAM. As a result, we show that it has the good classification accuracy. Specifically, the recall of the proposed method is 87.8% for English, 86.0% for Japanese and 72.8% for Chinese. Although it turned out that Chinese classification accuracy was extremely low in the present experiments compared with English and Japanese, the proposed method is language-independent and provides a new perspective and has excellent potential.

Asymptotic evaluation of distance measure on high dimensional vector spaces in text mining

This paper discusses the document classification problems in text mining from the viewpoint of asymptotic statistical analysis. In the problem of text mining, the several heuristics are applied to practical analysis because of its experimental effectiveness in many case studies. The theoretical explanation about the performance of text mining techniques is required and such thinking will give us very clear idea. In this paper, the performances of distance measures used to classify the documents are analyzed from the new viewpoint of asymptotic analysis. We also discuss the asymptotic performance of IDF measure used in the information retrieval field.

On a deductive reasoning model and method for uncertainty

Discusses a problem of deduction with uncertainty that has been dealt with by various diagnostic expert systems. First, we propose a mathematical framework of deductive reasoning with uncertainty. The subject of the reasoning is the calculation of conditional probabilities. Second, we establish a new reasoning method. Our deduction algorithm can compute the conditional probabilities precisely. To put it another way around, the result minimizes the divergence.

Refinement of index term set and improvement of classification accuracy on text categorization

In our previous paper, we proposed a new classification technique called the frequency ratio accumulation method (FRAM). This is a simple technique that adds up the ratios of term frequency among categories. However, in FRAM, the use of index terms is unlimited. Then, we adopt character N-gram as index terms improving the above-described particularity of FRAM. In the present paper, we will refine the DB of the index term set using mutual information and frequency ratio, and improve the classification accuracy. Next, the proposed method is evaluated by performing several experiments. In these experiments, we classify newspaper articles from English Reuters-21578 using FRAM. Reuters-21578 provides benchmark data in automatica text categorization. As a result, we show that it has the good classification accuracy. Specifically, the macro-averaged F-measure of the proposed method is 92.3% for Reuters-21578. Our method is language-independent and provides a new perspective and has excellent potential.

Low vision aid through laser retina imaging: Toward building eyesight-aid

Laser retina imaging technology enables creation of a clear picture in any retina area. It is focus-free, i.e. irrelevant of refractive disorder. So in many cases of low vision, one can see the picture by ones own eyesight. In this article we describe possible applications of this emerging technology, including

Current status of networked image communication system

Recent progress in medical imaging equipment besides evolution of each modality is the standardization of the image format, and communication method between them. When such an integrated diagnosis and therapy system is realized, operators must manipulate more than one equipment in order to receive the full benefit of the multiple-modality environment. This paper describes our basic concept, and some prototype design toward unified operation of networked medical equipment.

Clinical Evaluation Of TDF-500 System

For PACS to successfully replace the current film-based diagnosis system , there must be certain merits on PACS . None will use any system that may not work at least as smoothly as the current system . PACS is a big , expensive system . So , it must be at least equivalent or compatible to the current system from the performance point of view . We think the most promising way to achieve larger or full PACS is to create a system that has at least equal ability as the current film-based system , and then refine it so that the users really want to manipulate in their daily work . The point we must be aware of is that even if the prototype system is excellent in several points , when it should have defects in one point, this system may not be regarded as superior to the current system. Our way to establish PACS is to create a small system that has at least all-modality capability and high-speed processing power , and do a cooperate research on the real clinical environment and gather useful informations from them . Then we can develop new image processing methods , new mam-machine interface ,and any unforeseen features that are necessary to this kind of system . We have developed TDF-500 system from this viewpoint , and have also installed some of this system into real clinical environments . Overall reputations are good so far , and we are confident that PACS work station must meet the very basic but important features such as all-modality , multi-screen , and easy operation . Based on these informations , we will develop new technologies that must be necessary for future PACS .