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首页> 外文期刊>International Journal of Soft Computing and Software Engineering >An Efficient Decision Tree Classifier to Predict Precipitation Using Gain Ratio
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An Efficient Decision Tree Classifier to Predict Precipitation Using Gain Ratio

机译：利用增益比预测降水的高效决策树分类器

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The population of the world has been increasing substantially. The populous countries like India, seriously lagging behind to provide the basic needs to the people. Food is one of the basic needs that any country has to fulfill. Agriculture is one of the major sectors on which one third of Indian population depends on. The irrigation based countries like India where the water has been the basic resource that forges the plants’ growth. The main resource for the irrigation is rainfall which is scientifically a liquid form of precipitation. The atmospheric nimbus clouds are responsible for this precipitation. Prediction of the precipitation is necessary, as it has to be considered during the financial planning of a country. The meteorological departments of every nation are very keen in recording the datasets of precipitation which are huge in content. Hence, data mining is found to be an apt tool which would extract the relation between the datasets and their attributes. A Supervised Learning in Quest is one such data mining algorithm which is eventually a decision tree used to predict the precipitation based on the historical data. The Supervised Learning in Quest decision tree using gain ratio is a statistical analysis for establishing the relation between attribute set and precipitation which furnishes the prediction with an accuracy of 77.78%.
机译：世界人口大幅度增加。印度等人口大国在为人民提供基本需求方面严重落后。食物是任何国家必须满足的基本需求之一。农业是印度三分之一人口赖以生存的主要部门之一。像印度这样的以灌溉为基础的国家/地区，水一直是促进植物生长的基本资源。灌溉的主要资源是降雨，科学上讲是液体形式的降水。大气中的雨云是造成这种降水的原因。必须对降水进行预测，因为在一个国家的财务计划中必须考虑降水。每个国家的气象部门都非常热衷于记录内容丰富的降水数据集。因此，发现数据挖掘是一种合适的工具，它将提取数据集及其属性之间的关系。 Quest中的监督学习就是这样一种数据挖掘算法，它最终是用于根据历史数据预测降水的决策树。使用增益比率的Quest监督学习决策树是一种统计分析，用于建立属性集与降水之间的关系，从而以77.78％的准确性提供了预测。

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