Proceedings of the Third International Workshop on Active Mining (AM-2004) in Conjunction with the 18th Annual Conference of the Japanese Society for Artificial Intelligence, 2004 Organizers Hiroshi Motoda Takahira Yamaguchi Shusaku Tsumoto Masayuki Numao
Ishikawa Kousei Nenkin Kaikan, Kanazawa, JAPAN June 1, 2004
ORGANIZERS Hiroshi Motoda
Osaka University, Japan
Takahira Yamaguchi
Keio University, Japan
Shusaku Tsumoto
Shimane University, Japan
Masayuki Numao
Osaka University, Japan
PROGRAM COMMITTEE Hiroki Arimura
Hokkaido University, Japan
Stephen D. Bay
Stanford University, U.S.A.
Hendrik Blockeel
Katholieke Universiteit Leuven, Belgium
Robert H.P. Engels
CognIT, Norway
Shoji Hirano
Shimane University, Japan
Tu Bao Ho
JAIST, Japan
Akihiro Inokuchi
IBM Japan, Japan
Hiroyuki Kawano
Kyoto University, Japan
Boonserm Kijsirikul
Chulalongkorn University, Thailand
Ross D. King
The University of Wales, Aberystwyth
Yasuhiko Kitamura
Kwansei Gakuin University, Japan
Ravi Kothari
IBM - India Research Lab, India
Marzena Kryszkiewicz
Warsaw University of Technology, Poland
Tsau Y. Lin
San Jose State University, U.S.A.
Huan Liu
Arizona State University, U.S.A.
Masayuki Numao
Osaka University, Japan
Miho Ohsaki
Doshisha University, Japan
Takashi Okada
Kwansei Gakuin University, Japan
Takashi Onoda
CRIEPI, Japan
Yukio Ohsawa
University of Tsukuba, Japan
Luc de Raedt
University of Freiburg, Germany
Henryk Rybinski
Warsaw University of Technology, Poland
Masashi Shimbo
NAIST, Japan
Einoshin Suzuki
Yokohama National University, Japan
Yoshimasa Takahashi
Toyohashi University of Technology, Japan
Masahiro Terabe
MRI, Japan
Ljupico Todorovski
Jozef Stefan Institute, Slovenia
Shusaku Tsumoto
Shimane University, Japan
Stefan Wrobel
Fraunhofer AIS and University Bonn, Germany
Seiji Yamada
NII, Japan
Takahira Yamaguchi
Keio University, Japan
Yiyu Yao
University of Regina, Canada
Kenichi Yoshida
University of Tsukuba, Japan
Tetsuya Yoshida
Hokkaido University, Japan
SPONSORS Japanese Society for Artificial Intelligence Active Mining Project (Grant-in-Aid for Scientific Research on Priority Areas, No.759)
Program and Table of Contents
June 1st (Tuesday) 9:30-10:00
Registration
10:00-10:10
Opening Session 1: Mining Medical Data
10:10-10:40
Spiral Discovery of a Separate Prediction Model from Chronic Hepatitis Data Masatoshi Jumi, Einoshin Suzuki, Muneaki Ohshima, Ning Zhong, Hideto Yokoi, Katsuhiko Takabayashi ................................................... 1
10:40-11:10
Evaluation of Rule Interestingness Measures with a Clinical Dataset on Hepatitis Miho Ohsaki, Shinya Kitaguchi, Kazuya Okamoto, Hideto Yokoi, Takahira Yamaguchi..............................................................................11
11:10-11:40
Process to Discovering Iron Decrease as Chance to Use Interferon to Hepatitis B Yukio Ohsawa, Hajime Fujie, Akio Saiura, Naoaki Okazaki, Naohiro Matsumura ............................................................................. 21
11:40-12:10
Preliminary Analysis of Interferon Therapy by Graph-Based Induction Tetsuya Yoshida, Warodom Geamsakul, Akira Mogi, Kouzou Ohara, Hiroshi Motoda,Takashi Washio, Hideto Yokoi, Katsuhiko Takabayashi ............................................................................................................... 31
12:10-13:00
Lunch Session 2: Mining and Information Gathering
13:00-13:30
A Novel Hybrid Approach for Interestingness Analysis of Classification Rules Tolga Aydin and Halil Altay Guvenir ................................................... 41
13:30-14:00
Preliminary Evaluations of Discovered Rule Filtering Methods Yasuhiko Kitamura, Akira Iida, and Keunsik Park............................ 53
14:00-14:30
Proposal of Relevance Feedback based on Interactive Keyword Map Yasufumi Takama and Tomoki Kajinami ............................................ 63
14:30-14:50
Coffee break Session 3: Mining Chemical Compound Structure
14:50-15:20
A Correlation-Based Approach to Attribute Selection in Chemical Graph Mining Takashi Okada ...................................................................................... 73
15:20-15:50
Combining
Partial
Rules
and
Winnow
Algorithm:
Results
on
Classification of Dopamine Antagonist Molecules Sukree Sinthupinyo, Cholwich Nattee, Masayuki Numao, Takashi Okada, Boonserm Kijsirikul................................................... 83 15:50-16:20
Identification of Activity Classes of Drugs under Existing Noise Compounds by ANN and SVM Yoshimasa Takahashi, Satoshi Fujishima, Katsumi Nishikoori, Hiroaki Kato, Takashi Okada .............................................................. 93
16:20-16:50
Mining of Three-Dimensional Structural Fragments in Drug Molecules Hiroaki Kato, Takashi Koshika, Yoshimasa Takahashi, Hidetsugu Abe..................................................................................... 102
16:50-17:00
Closing
Spiral Discovery of a Separate Prediction Model from Chronic Hepatitis Data Masatoshi Jumi1 , Einoshin Suzuki1 , Muneaki Ohshima2 , Ning Zhong2 , Hideto Yokoi3 , and Katsuhiko Takabayashi3 1. Electrical and Computer Engineering, Yokohama National University, Japan 2. Faculty of Engineering, Maebashi Institute of Technology, Japan 3. Division of Medical Informatics, Chiba University Hospital, Japan
[email protected],
[email protected],
[email protected],
[email protected],
[email protected],
[email protected]
Abstract. In this paper, we summarize our endeavor for spiral discovery of a separate prediction model from chronic hepatitis data. We have initially proposed various learning/discovery methods including time-series decision tree, PrototypeLines, and peculiarity-oriented mining method for mining the data. This experience has led us to believe that physicians tend to consider typical cases with the specific disease and rule out from the clearly exceptional cases. We have developed a spiral discovery system which learns a prediction model for each type of cases, and obtained promising results from experiments.
1
Introduction
Medical data are challenging to data miners since they show problems related to data quantity, data quality, and data form [5]. Chronic hepatitis data [1], which show high diversities in various aspects of cases, satisfy this nature and necessitate us to develop novel data mining methods. For the chronic hepatitis data, providers presented several objectives including progress of chronic hepatitis (difference between type B and type C) and effect of interferon therapy. Among them, LC (liver cirrhosis) prediction from blood test data can be regarded as one of the most important objectives since it can potentially substitute routine tests for a biopsy, which represents a highly invasive test. We have been working on knowledge discovery from the chronic hepatitis data including the LC prediction. Our methods include a decision tree learner for time-series classification problem [5], a visualization method for medical test data [4], and a peculiarity-oriented mining method [6]. The methods have proved to be effective for various purposes including detection of exceptional cases. From the endeavor, we have come to believe that physicians tend to start their differential diagnoses by distinguishing typical cases from apparently exceptional cases. In this paper, we propose a discovery method which distinguishes the two types of cases in its separate prediction model based on the abovementioned data mining methods.
1
2
Knowledge Discovery from the Chronic Hepatitis Data
2.1
Liver Cirrhosis Prediction
Chronic hepatitis represents a disease in which liver cells become inflamed and harmed by virus infection. In case the inflammation lasts a long period, the disease comes to an end which is called a liver cirrhosis (LC). During the process to an LC, the degree of fibrosis, which consists of five stages ranging from F0 (no fibrosis) to F4 (LC), represents an index of the progress. The degree of fibrosis can be inspected by biopsy which picks liver tissue by inserting an instrument directly into liver. A biopsy, however, cannot be frequently performed since it requires a short-term admission to a hospital and involves danger such as hemorrhage. Therefore, if we can predict the degree of fibrosis with a conventional medical test such as a blood test, it would be highly beneficial in medicine. A time sequence A represents a list of values α1 , α2 , · · · , αI sorted in chronological order. A data set D consists of n examples e1 , e2 , · · · , en , and each example ei is described by m attributes a1 , a2 , · · · , am and a class attribute c. We assume that an attribute aj represents a time-series attribute which takes a time sequence as its value1 . The class attribute c represents a nominal attribute and its value is called a class. We show an example of a data set which consists of time-series attributes in Figure 1. The data set consists of examples 84, 85, 930, each of which is described with time-series attributes GPT, ALB, PLT, and a class. time-series attributes
GPT
ALB
200
examples 84
PLT
6
300
5 100
200 4
0 -500 -250
0
250 500
200
0
250
500
6
250 500
200
0
250 500
non-LC
100
3 -500 -250
0
250
500
6
0 -500 -250
0
250 500
400 300
5 100
200 4
0 -500 -250
0
300
4
200
930
0 -500 -250 400
100
0 -500 -250
non-LC
100
3 -500 -250
5
85
class
400
0
250 500
LC
100
3 -500 -250
0
250
500
0 -500 -250
0
250 500
Fig. 1. Data set which consists of time-series attributes
In classification from time-series data, the objective represents induction of a classifier, which predicts the class of an example e, given a training data set D. 1
Though it is straightforward to include other kinds of attributes in the definition, we exclude them for clarity.
2
In this paper, we assume liver cirrhosis (LC) and non-liver cirrhosis (non-LC) as classes. 2.2
Our Endeavor for the Chronic Hepatitis Data
Our time-series decision tree was proposed since physicians requested to use time sequences which exist in data in a classifier [5]. We applied the method to the LC prediction problem and the results attracted their interests. The physicians commented that the obtained decision tree is highly valid although we used medical knowledge only for selecting medical tests in the data [5]. Moreover, most of the cases who are mispredicted by the decision tree were recognized as exceptions by the physicians. Our PrototypeLines represents a visualization method which is considered to enable discovery of interesting knowledge without extensive training [4]. Application of PrototypeLines to the chronic hepatitis data revealed interesting characteristics. A student in computer science discovered at least two exceptions which were both recognized as missing diseases in the original data by a domain expert. Moreover, a physician who was introduced PrototypeLines for the first time discovered an exception condition of a case after a 5-minute explanation. Our peculiarity-oriented mining method obtains tendencies among examples with peculiar data [6]. The peculiar data are detected based on a distance measure, and the number of the peculiar data for an example is called the number of peculiar attributes. The method has successfully discovered exceptional cases in terms of interferon therapy.
3 3.1
Discovery of a Separate Prediction Model Overall Architecture
Based on the motivation presented in Section 1, we propose a discovery method which learns a separate prediction model. Our method employs a naive Bayes learner and a 1-NN (1-nearest neighbor) classification method for typical cases and exceptional cases respectively, and refines the separate prediction model by a spiral interaction with a medical expert. The term “spiral” is employed since it represents iterative refinement of discovered knowledge. In the interaction, exceptional cases are identified with the data mining methods mentioned in Section 2.2 and a likelihood-based method to be proposed in the next Section. Figure 2 shows the architecture of our method. In our method, detection of exceptional cases with our previous methods as well as update of a naive Bayes learner for typical cases are iterated in a spiral manner with interaction with an expert. A naive Bayes classifier predicts a class cˆN Bayes,i of an example ei assuming that each attribute aj is independent. Here vij represents the value for attribute aj of p. cˆN Bayes,i = argmaxc Pr(c)
m j=1
3
Pr(aj = vij |c)
(1)
As the result, we obtain a list of typical cases, conditional probabilities for typical cases, and a list of exceptional cases. Classification of a new case begins by detection of exceptional cases based on a 1-NN method, which will be presented in Section 3.3. If a case who is close to the new case is found, our method outputs the class of the former case. Otherwise, the class of the new case is predicted with a naive Bayes classifier.
Prototypelines
peculiarity oriented mining method
interaction
expert
time-series decision tree
detection of exceptional cases
spiral iteration
odds-based judgement
NB learner learning list of typical cases
conditional probabilities NB classifier
prediction 2
list of exceptional cases
prediction 1 1-NN for time-series classification
list of novel cases
Fig. 2. Architecture of the proposed method
3.2
Likelihood-based Judgment for Exception Degrees
In a classification problem, an example can be intuitively regarded as “typical” or “atypical”. The typicalness Φ(p), which is based on the right-hand side of Eq. (1), of a case p represents a degree to which p belongs to its class c compared with the other class c. Here vij represents the value for attribute aj of p. Pr(aj = vij | c) Pr(c) m j=1 (2) Φ(p) = m Pr(c) j=1 Pr(aj = vij | c) The larger Φ(p) is, the more certain that p belongs to its class c. If a naive Bayes classifier is relatively accurate for the class c, the typicalness Φ(p) tends to be large and vice versa. Thus the typicalness Φ(p) is relative since it depends on the preciseness of a naive Bayes classifier in terms of the class c. The degree how a naive Bayes classifier is precise for c can be measured by the degree of correct classification and the degree of incorrect classification. For c and c, we represent the number of correctly-predicted examples by the naive Bayes method L and n respectively. Likewise, for c and c, we represent the number of incorrectly-predicted examples by the naive Bayes method Le and
4
ne respectively. The preciseness Ψ (ˆ c) of a naive Bayes classifier of the estimated class cˆ represents the ratio of the precision for c and the precision for c. Ψ (ˆ c) =
(L + 1)(n + ne + 2) (L + Le + 2)(ne + 1)
(3)
For our LC prediction problem, we define a degree of exception E(p, cˆ) for a case p and his/her estimated class cˆ as follows in order to discriminate exceptional cases from typical cases. E(p, cˆ) = − logΨ (ˆc) Φ(p)
(4)
Intuitively, E(p, cˆ) represents an evaluation index which is equal to the number of upvaluated digits below the decimal point when we measure the typicalness Φ(p) of a case p in terms of the preciseness Ψ (ˆ c) of a naive Bayes classifier of the estimated class cˆ. When prediction of the naive Bayes method is accurate, Ψ (ˆ c) tends to be large, and the absolute value of E(p, cˆ) is relatively small even if the absolute value of Φ(p) is large. This fits our intuition that certain information rarely leads to an extreme degree of exception. In each loop, cases whose degrees of exception are no less than a user-specified threshold are detected as exceptional cases. The loop continues until prediction accuracy estimated with 10-fold cross validation either decreases or reaches 100 %. 3.3
Similarity between a Pair of Cases Based on Dynamic Time Warping
Dynamic time warping (DTW) represents an index of dissimilarity between a pair of time sequences [3]. Unlike Euclidean distance, DTW can allow distortion along the time axis since a point in a time sequence can correspond to multiple points in the other sequence. A 1-NN method based on DTW constantly showed high accuracy in our experiments for time-series classification including the LC prediction problem [5]. Due to this good result, we have chosen this method for detecting exceptional cases in test data. Following [5], the window width [3] of DTW was settled to 10 % of the length of the time sequence, and a dissimilarity measure H(ei , ej ) between a pair of examples ei , ej has been employed. This measure normalizes the results G(ei (ak ), ej (ak )) of DTW for ei and ej in terms of a time-series attribute ak with the maximum value q(ai ), where q(ai ) ≥ ∀j∀kG(e(aj ), e(ak )). H(ei , ej ) =
m G(ei (ak ), ej (ak ))
q(aj )
k=1
4 4.1
(5)
Experiments Application of Conventional Methods
In the experiments, we used data from 180 days before the first biopsy to the day of the first biopsy following advice of medical experts. As the result, the num-
5
bers of LC and non-LC cases are 159 and 112 respectively. The blood tests that we use are GOT (glutamic oxaloacetic transaminase = AST (asparate aminotransferase)), GPT (glutamic pyruvic transaminase = ALT (alanine aminotransferase)), TTT (thymol turbidity test), ZTT (Zinc sulfate turbidity test), D-BIL (direct bilirubin), I-BIL (indirect bilirubin), T-BIL (total bilirubin), ALB (albumin), CHE (cholineesterase), TP (total protein), T-CHO (total cholesterol), WBC (white blood cell), PLT (platelet), and HGB (hemoglobin). In our experiment, we used 46 LC cases and 55 non-LC cases each of whom has test values for all of these blood tests. For the classifier, we first averaged each time sequence then discretized each value as we show in Table 1, where we use for each attribute value U: extremely high, V: very high, H: high, N: normal, L: low, v: very low, and u: extremely low. Each conditional probability is estimated using Laplace correction in order to cope with the 0-occurrence problem [2]. A missing value is ignored both in estimating probabilities and in classifying an example.
Table 1. Blood tests in the chronic hepatitis data
attribute GOT GPT TTT ZTT D-BIL I-BIL T-BIL ALB CHE TP T-CHO WBC PLT HGB
intuitive explanation amount of broken liver cells degree of immune activity disorder of bile excretion decrease of protein generation
good order of a liver hemoglobin
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