• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Progress in Medical Physics
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• v.22 no.1
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• pp.42-51
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• 2011

Nuclear medicine images (SPECT, PET) were widely used tool for assessment of myocardial viability and perfusion. However it had difficult to define accurate myocardial infarct region. The purpose of this study was to investigate methodological approach for automatic measurement of rat myocardial infarct size using polar map with adaptive threshold. Rat myocardial infarction model was induced by ligation of the left circumflex artery. PET images were obtained after intravenous injection of 37 MBq $^{18}F$-FDG. After 60 min uptake, each animal was scanned for 20 min with ECG gating. PET data were reconstructed using ordered subset expectation maximization (OSEM) 2D. To automatically make the myocardial contour and generate polar map, we used QGS software (Cedars-Sinai Medical Center). The reference infarct size was defined by infarction area percentage of the total left myocardium using TTC staining. We used three threshold methods (predefined threshold, Otsu and Multi Gaussian mixture model; MGMM). Predefined threshold method was commonly used in other studies. We applied threshold value form 10% to 90% in step of 10%. Otsu algorithm calculated threshold with the maximum between class variance. MGMM method estimated the distribution of image intensity using multiple Gaussian mixture models (MGMM2, ${\cdots}$ MGMM5) and calculated adaptive threshold. The infarct size in polar map was calculated as the percentage of lower threshold area in polar map from the total polar map area. The measured infarct size using different threshold methods was evaluated by comparison with reference infarct size. The mean difference between with polar map defect size by predefined thresholds (20%, 30%, and 40%) and reference infarct size were $7.04{\pm}3.44%$, $3.87{\pm}2.09%$ and $2.15{\pm}2.07%$, respectively. Otsu verse reference infarct size was $3.56{\pm}4.16%$. MGMM methods verse reference infarct size was $2.29{\pm}1.94%$. The predefined threshold (30%) showed the smallest mean difference with reference infarct size. However, MGMM was more accurate than predefined threshold in under 10% reference infarct size case (MGMM: 0.006%, predefined threshold: 0.59%). In this study, we was to evaluate myocardial infarct size in polar map using multiple Gaussian mixture model. MGMM method was provide adaptive threshold in each subject and will be a useful for automatic measurement of infarct size.

• Journal of Korean Society of Forest Science
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• v.52 no.1
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• pp.58-71
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• 1981

Thecodiplosis japonesis is sweeping the Pinus densiflora forests from south-west to north-east direction, destroying almost all the aged large trees as well as even the young ones. The front line of infestation is moving slowly but ceaselessly norhwards as a long bottle front. Estimation is that more than 40 percent of the area of P. densiflora forest has been damaged already, however some individuals could escapes from the damage and contribute to restore the site to the previous vegetation composition. When the stands were attacked by this insect, the drastic openings of the upper story of tree canopy formed by exclusively P. densiflora are usually resulted and some environmental factors such as light, temperature, litter accumulation, soil moisture and offers were naturally modified. With these changes after insect invasion, as the time passes, phytosociologic changes of the vegetation are gradually proceeding. If we select the forest according to four categories concerning the history of the insect outbreak, namely, non-attacked (healthy forest), recently damaged (the outbreak occured about 1-2 years ago), severely damaged (occured 5-6 years ago), damage prolonged (occured 10 years ago) and restored (occured about 20 years ago), any directional changes of vegetation composition could be traced these in line with four progressive stages. To elucidate these changes, three survey districts; (1) "Gongju" where the damage was severe and it was outbroken in 1977, (2) "Buyeo" where damage prolonged and (3) "Gochang" as restored, were set, (See Tab. 1). All these were located in the south temperate forest zone which was delimited mainly due to the temporature factor and generally accepted without any opposition at present. In view of temperature, the amount and distribution of precipitation and various soil factor, the overall homogeneity of environmental conditions between survey districts might be accepted. However this did not mean that small changes of edaphic and topographic conditions and microclimates can induce any alteration of vegetation patterns. Again four survey plots were set in each district and inter plot distance was 3 to 4 km. And again four subplots were set within a survey plot. The size of a subplot was $10m{\times}10m$ for woody vegetation and $5m{\times}5m$ for ground cover vegetation which was less than 2 m high. The nested quadrat method was adopted. In sampling survey plots, the followings were taken into account: (1) Natural growth having more than 80 percent of crown density of upper canopy and more than 5 hectares of area. (2) Was not affected by both natural and artificial disturbances such as fire and thinning operation for the past three decades. (3) Lower than 500 m of altitude (4) Less than 20 degrees of slope, and (5) Northerly sited aspect. An intensive vegetation survey was undertaken during the summer of 1980. The vegetation was devided into 3 categories for sampling; the upper layer (dominated mainly by the pine trees), the middle layer composed by oak species and other broad-leaved trees as well as the pine, and the ground layer or the lower layer (shrubby form of woody plants). In this study our survey was concentrated on woody species only. For the vegetation analysis, calculated were values of intensity, frequency, covers, relative importance, species diversity, dominance and similarity and dissimilasity index when importance values were calculated, different relative weights as score were arbitrarily given to each layer, i.e., 3 points for the upper layer, 2 for the middle layer and 1 for the ground layer. Then the formula becomes as follows; $$R.I.V.=\frac{3(IV\;upper\;L.)+2(IV.\;middle\;L.)+1(IV.\;ground\;L.)}{6}$$ The values of Similarity Index were calculated on the basis of the Relative Importance Value of trees (sum of relative density, frequency and cover). The formula used is; $$S.I.=\frac{2C}{S_1+S_2}{\times}100=\frac{2C}{100+100}{\times}100=C(%)$$ Where: C = The sum of the lower of the two quantitative values for species shared by the two communities. $S_1$ = The sum of all values for the first community. $S_2$ = The sum of all values for the second community. In Tab. 3, the species composition of each plot by layer and by district is presented. Without exception, the species formed the upper layer of stands was Pinus densiflora. As seen from the table, the relative cover (%), density (number of tree per $500m^2$), the range of height and diameter at brest height and cone bearing tendency were given. For the middle layer, Quercus spp. (Q. aliena, serrata, mongolica, accutissina and variabilis) and Pinus densiflora were dominating ones. Genus Rhodedendron and Lespedeza were abundant in ground vegetation, but some oaks were involved also. (1) Gongju district The total of woody species appeared in this district was 26 and relative importance value of Pinus densiflora for the upper layer was 79.1%, but in the middle layer, the R.I.V. for Quercus acctissima, Pinus densiflora, and Quercus aliena, were 22.8%, 18.7% and 10.0%, respectively, and in ground vegetation Q. mongolica 17.0%, Q. serrata 16.8% Corylus heterophylla 11.8%, and Q. dentata 11.3% in order. (2) Buyeo district. The number of species enumerated in this district was 36 and the R.I.V. of Pinus densiflora for the uppper layer was 100%. In the middle layer, the R.I.V. of Q. variabilis and Q. serrata were 8.6% and 8.5% respectively. In the ground vegetative 24 species were counted which had no more than 5% of R.I.V. The mean R.I.V. of P.densiflora ( totaling three layers ) and averaging four plots was 57.7% in contrast to 46.9% for Gongju district. (3) Gochang-district The total number of woody species was 23 and the mean R.I.V. of Pinus densiflora was 66.0% showing greater value than those for two former districts. The next high value was 6.5% for Q. serrata. As the time passes since insect outbreak, the mean R.I.V. of P. densiflora increased as the following order, 46.9%, 57.7% and 66%. This implies that P. densiflora was getting back to its original dominat state again. The pooled importance of Genus Quercus was decreasing with the increase of that for Pinus densiflora. This trend was contradict to the facts which were surveyed at Kyonggi-do area (the central temperate forest zone) reported previously (Yim et al, 1980). Among Genus Quercus, Quercus acutissina, warm-loving species, was more abundant in the southern temperature zone to which the present research is concerned than the central temperate zone. But vice-versa was true with Q. mongolica, a cold-loving one. The species which are not common between the present survey and the previous report are Corpinus cordata, Beltala davurica, Wisturia floribunda, Weigela subsessilis, Gleditsia japonica var. koraiensis, Acer pseudosieboldianum, Euonymus japonica var. macrophylla, Ribes mandshuricum, Pyrus calleryana var. faruiei, Tilia amurensis and Pyrus pyrifolia. In Figure 4 and Table 5, Maximum species diversity (maximum H'), Species diversity (H') and Eveness (J') were presented. The Similarity indices between districts were shown in Tab. 5. Seeing Fig. 6, showing two-dimensional ordination of polts on the basis of X and Y coordinates, Ai plots aggregate at the left site, Bi plots at lower site, and Ci plots at upper-right site. The increasing and decreasing patterns as to Relative Density and Relative Importance Value by genus or species were given in Fig. 7. Some of the patterns presented here are not consistent with the previously reported ones (Yim, et al, 1980). The present authors would like to attribute this fact that two distinct types of the insect attack, one is the short war type occuring in the south temperate forest zone, which means that insect attack went for a few years only, the other one is a long-drawn was type observed at the temperate forest zone in which the insect damage went on continuously for several years. These different behaviours of infestation might have resulted the different ways of vegetational change. Analysing the similarity indices between districts, the very convincing results come out that the value of dissimilarity index between A and B was 30%, 27% between B and C and 35% between A and C (Table 6). The range of similarity index was obtained from the calculation of every possible combinations of plots between two districts. Longer time isolation between communities has brought the higher value of dissimilarity index. The main components of ground vegetation, 10 to 20 years after insect outbreak, become to be consisted of mainly Genus Lespedeza and Rhododendron. Genus Quercus which relate to the top dorminant state for a while after insect attack was giving its place to Pinus densiflora. It was implied that, provided that the soil fertility, soil moisture and soil depth were good enough, Genus Quercuss had never been so easily taken ever by the resistant speeies like Pinus densiflora which forms the edaphic climax at vast areas of forest land. Usually they refer Quercus to the representative component of the undisturbed natural forest in the central part of this country.