MTS can separate normal samples from abnormal samples by using Mahalanobis distance,which can take into account the correlation between features,and select features with larger information gain through signal-to-noise ratio and orthogonal arrays. As a data-driven pattern recognition method,MTS has been widely used in manufacturing cost accounting , automobile motor-head machining process,rolling bearing fault diagnosis and health assessment and management decision-making.So,this paper will propose a recognition method of multidimensional poverty based on MTS. Good health and education are pivotal aspects of human capability as well,comprehended in Sen’s theory as freedom of guiding a person to have a different type of life. It is noteworthy that improving sanitation facilities has important positive effects on reducing the contagion of various diseases like hepatitis, cholera, and diarrhea. The three education indexes are access to improved educational attainment, vertical grow table reduced expenditure for education, and accessibility of public education. Besides, there are four indexes to measure deprivation of health, such as health conditions, Sanitation facilities, expenditure for health, and accessibility of healthcare.
The reason for including these dimensions is that with rural,income does not assure to get education and health services. Living dimension is acknowledged as a standard to measure access to basic services, which is included four indexes, such as clean water, improved cooking fuel, electricity, and Engel’s coefficient. Among them, unsafe water can cause many diseases in rural,and the availability of safe water is to a fundamental human right. Good electricity can help people improve accessibility to information by using a wide range offacilities like television, refrigerators, telephones, and computers. In China, using solid fuel caused by indoor air pollution is the primary reason for more than40,000 premature deaths annually. Finally, we consider that asset and housing dimensions are also essential to enhance the quality of life. These reflect the rate of accumulation assets of rural families and provide a buffer territory for people to relieve the negative effects of social and economic risks. There are four assets indexes to evaluate household capital accumulation, such as means of production,assisted living assets, cultivated land quantity, types of current housing. In addition, we use seven indexes to reflect housing conditions, such as Per capita housing, congestion, healthy conditions, lighting conditions, ventilate conditions,air condition, and noisy conditions.
The dataset used in this paper is from the China Labor-force Dynamic survey in 2014, which surveyed the working population aged 15 – 64. It is an interdisciplinary large-scale survey, including issues of labor education, employment,household property and income, household consumption, production and land of the rural household.CLDS2014uses multi-stage and multi-level probability sampling method, which can better reflect the real situation of Chinese society. This paper selected rural household data from central provinces of China in CLDS2014 for analysis,mobile vertical grow tables including Anhui, Henan, Hunan, Hubei, and Jiangxi. The central provinces have the characteristics of large agricultural population,wide distribution of poor people, and complex and diverse causes of poverty. Therefore, this sample data is selected for recognition in the hope of providing a reference for poverty alleviation.
The households were taken as the basic unit to analyze. Besides, we selected family-level data as the primary data source of this study. Nevertheless, we turned to the individual-level data for more detailed information when the description about the conditions of members was ambiguous in the family-level data. Firstly, we segregated rural households from urban households in term of where they lived in, village committees or neighborhood committees. Secondly, if families rejected answering significant problems corresponding to the study, we would deleteit. Thirdly, we preserved families with total income/expenditure are equal to the sum of income/expenditure from sub-component sources. Finally, we got 425 households. In this paper, 425 families are recognized as two classes, that is, poor and non-poor by using the MTS. The MTS can recognize poor and non-poor households and select the main indexes to measure multidimensional poverty, which mainly focuses on income, health, and housing conditions. For rural families, it was of significance to raise their income or enhance sustainable livelihood capacity. Improving farming efficiency was the most remunerative strategy which required relatively high technical support and financial input.