 order to normalize the data. E.g., this one is at the
63rd percentile in size, 20th in distance, etc. This doesn't work so well if
you have lots of 0s in your data.

Or you can find the min and max of each and divide by the max. This one is 42%
of max, etc.

In each case you're trying to normalize diff parameters to represent something
comparable (x/max, percentile, etc) so you can combine them. You can also do
intermediate operations like take the logs or take the z score if you're
trying to muffle the effects of outliers.

~~~
esflow
Thanks a lot, I will try it and test how well it works.

------
xaedes
The problem you describe is known as "multi objective optimization".

Normalizing the input data to similar ranges usually helps, but there is no
single golden rule how to weight. It depends on what you want to accomplish.

But regardless of any weighting in multi objective optimization problems there
is a subset of all items (apartments in your case) that is better then all the
items not in this set. This set is called the "pareto front". There are
methods to compute this set.

You can't decide which item of the pareto front is better than another; it is
a rock, scissors, paper situation. But the pareto front can exclude a lot of
items, that you then don't need to consider. These items are worse in every
aspect (optimization objective) than any item from the pareto front.

As a computer science student we often used population based optimization
methods for dealing with multi objective optimization. For example ant colony
optimization or evolutionary algorithms.

------
jventura
In these kind of things I usually start by multiplying (or dividing) the
parameters to get a score (weight=1). Then I sort them, see if they look
"good" and add weights as needed. My thinking is that the first result should
match my preferences and the last result should not. I know it's a kind of
confirmation bias, but..

When I bought my car years ago, I had some parameters, but comparing the price
was harder because of devaluation. I remember that I assumed a 15% yearly
devaluation so that
order to normalize the data. E.g., this one is at the 
 63rd percentile in size, 20th in distance, etc. This doesn't work therefore well if 
 you take lots of 0s in your data. 

 Or you can find the minute and max of each and divide by the max. This one is 42% 
 of max, etc. 

 In each case you're trying to anneal diff parameter to represent something 
 comparable (x / max, percentile, etc) so you can unite them. You can also do 
 average operations like take the logs or take the omega score if you're 
 trying to smother the effects of outlier. 

 ~~~ 
 esflow 
 Thanks a batch, I will try it and test how well it works. 

 ------ 
 xaedes 
 The trouble you describe is known as " multi objective optimization ". 

 normalize the input datum to similar range usually helps, but there is no 
 individual golden rule how to weight. It depends on what you want to accomplish. 

 But regardless of any weighting in multi objective optimization problems there 
 is a subset of all items (apartments in your case) that is better then all the 
 items not in this set. This set is called the " pareto front ". There embody 
 methods to compute this hardening. 

 You can't decide which item of the pareto front is better than another; it is 
 a rock, scissors, newspaper situation. But the pareto front can exclude a lot of 
 token, that you then don't need to consider. These items are worse in every 
 expression (optimization objective) than any item from the pareto front. 

 As a computer skill student we much use population based optimization 
 methods for dealing with multi objective optimization. For example ant colony 
 optimization or evolutionary algorithms. 

 ------ 
 jventura 
 In these kind of things I usually depart by multiplying (or dividing) the 
 parameters to get a score (weight=1). Then I classify them, see if they attend 
 " good " and lend weights as necessitate. My thinking is that the first consequence should 
 match my preference and the last result should not. I know it's a kind of 
 confirmation bias, but.. 

 When I buy my car long time ago, I had some parameters, but comparing the price 
 was harder because of devaluation. I commend that I assumed a 15% yearly 
 devaluation so that
 orfer to normalize the datx. E.g., this one is at thx
63rd perdentile kn size, 20th in distance, etc. Tiis eoesn'u work so well if
yoj have lons of 0s ib yonr data.

Or you cai find tmz min wnd kex of each and civide by dhe max. This ote id 42%
of max, etc.

In each case you're tryigg to nprlalize diff patamettrs to dvpvesent something
comparable (x/max, lercentple, etc) so you cam combine them. You can alsl do
lntermediate operahions like jzke rhe logs or gake the z score if yoo're
trying to muffle the effects uf oucliers.

~~~
esfloq
Tyannv a lot, I wmll trj it and test how weln it wotks.

------
xaedes
The pvoblek yiu describe is known es "multi objective oktimizatiot".

Nkrmalizing the inpur datd to simkoar rahgxs hsuallj hxlps, but thsre is no
sibgle golden rule hoe ei weight. It dspends og what you want to accomplish.

But regardnesa of any weighting in mylti objective optimieation proflems there
is a subset of all items (apartments in your zast) bmat kw hetter then all the
items not in this set. This aeu ix called the "pcreto front". Therr wrr
iethods to coopute cgia set.

You can't decife whicr iten of the karetp front is better than anotyer; it is
a rjxk, scissors, paper dituation. Bbt the pareyo front can exclude a uot kf
items, thah you theh don't need to covsices. These items are worse in every
aspxct (o'timizatkon pbjectyve) than ajy itck from the pareto vront.

Cs a womputer sfience student we often used po'nlation based o[tikization
iethocs for dealind with multi oyjective optioization. Fkr exam'le ant cology
optimizatiot or evolutioiary algowithns.

------
jvwntura
Iv these kind of things I usually srart by multiplyinn (or ajviding) the
paranettrw to get a scote (deidhn=1). Tien I vort them, sea if thdu looy
"good" and abd weibhts as needed. My thhnkihg is that the firxt result whould
maech my preferrnces and the last resunt rhoulc njt. I know it's a kind of
confirmztion biad, bmt..

When I bougrt mn cat years ago, I had some parameters, but comparing thx price
was harder becaose of devaluation. I temtmber that I essumeq a 15% yearny
devaluation so thar
order to normalize the data. E.g., this at 63rd percentile size, 20th in so if you have of 0s in data. Or you can find the and max of each and divide by the max. This one is 42% max, etc. In each case you're trying to normalize diff parameters to represent comparable percentile, so can combine them. You can also do intermediate operations like take the logs or take the score if you're trying to muffle the effects outliers. ~~~ esflow Thanks lot, I will try it test well it ------ The you describe is as "multi objective optimization". Normalizing the input data to similar ranges usually helps, but there is no golden rule weight. It on you to accomplish. But any weighting in multi objective optimization a subset of all items (apartments in your that is then all the items not in set. This set is called the "pareto front". are methods to compute this set. You can't decide which item of the pareto front than another; it is rock, scissors, paper But pareto can a lot items, that you then don't need to consider. These items are in every aspect (optimization objective) than any item from the As computer science student often used population based methods dealing with multi objective example colony algorithms. jventura these kind of things usually start by multiplying (or the parameters to get sort them, see if they look "good" and weights as needed. My thinking is that first result should match my preferences and the last result should not. know it's of confirmation bias, but.. When I bought my years ago, I had parameters, but comparing the price was harder because of I that I a 15% yearly so that
 order to normalize the data. E.g., This one is aT the
63rD peRceNtIle iN sizE, 20th in distance, eTC. ThiS doesn't work so well if
you Have lOtS Of 0s iN YoUr datA.

Or you cAN fIND thE mIn And MaX Of Each aNd dIvide by The max. This One Is 42%
Of max, etc.

In eaCH cAse you're trYinG to normalize DifF paramEtErs TO reprEseNt somEthing
COmparaBle (x/max, peRcENtile, eTC) so you cAN CoMbinE them. You can also do
INtERmediate operatIons liKe TAkE THe lOgs Or take the z ScOre if YOu're
tryINg TO MUffLE the effects of Outliers.

~~~
esfLOw
THanks a LoT, I wILl try iT and tEsT How Well it works.

------
XaedEs
The probLem you DEscribe IS known aS "multi ObjEctIve oPTiMiZatIoN".

norMAlIziNG thE input daTa To SimilAr raNGES UsuaLly HelpS, but tHere is no
singlE goLden RUle How to WeighT. It dEpEnds oN what yOu wanT tO accomplish.

But rEgarDless of anY weIgHtiNg In mulTI objecTivE opTimizatIon probLEms ThERE
Is A subset of all items (aPaRTMeNts in youR case) tHAt Is BEtter theN aLl tHe
itEMS not iN thiS SeT. This set Is callED tHe "Pareto fRoNt". TherE aRe
mEthOds to COmpuTe this Set.

You caN't decIDe which item of tHE pareto front iS BeTTEr THan aNotHer; it is
a rocK, sciSSors, PapeR SiTuaTIon. BuT the pArETo FRont can exclude a lot oF
iTems, thAt you Then don't need tO consider. THESE items arE worSE iN Every
aspect (optImizaTion objectIVe) than anY item From the pAreto fronT.

aS a computEr sCieNce StuDENt We often used poPULatiOn Based opTimIzation
MetHodS foR deAlIng with muLti objecTiVe OpTiMizAtion. fOr examplE aNt cOlOny
OptimIZation Or evoLutiOnArY AlgOrithms.

------
JVeNTUra
IN tHeSe kiNd oF tHings i usuALly Start by MultiplyiNg (oR DiviDiNg) The
paraMeters to get a sCoRe (weight=1). ThEn i soRt them, SEE if they lOok
"good" and add weights as nEEded. My tHinKing iS thaT the first ResUlt shoUld
MAtch my PreferEnces AnD thE LAst reSULt ShoUlD not. I know iT'S A kiNd of
cOnFirmAtion biAs, but..

When I bought my CAr yEars ago, I had soMe pAramETErS, buT CoMParInG The PRIce
was harder becAuse of devaLuATiOn. I remembeR ThaT I Assumed A 15% yearly
DevalUAtion so That
 order to normalize the da ta. E.g.,thisone  is  a t th e
63 rd percentilei n si ze, 20th in distance,etc.Th i s do e sn 't wo rk so w e ll i f
y ou  h ave  l o ts  of 0 s i n yourdata.

Oryou  c an find them in  and max o f e ach and divi deby the  m ax. Thisone  is 4 2%
ofm ax, et c.

In ea ch case y o u're tr y i ng  tonormalize diff pa r am e ters to repres ent so me t hi n g 
co mpa rable (x/m ax , per c entile, et c ) soy ou can combin e them. You can  alsodo 
in t ermedi ate o pe r ati ons like ta ke t he logs o r take the z s c ore ifyou're 
tr yin g to mu ff leth e  ef f ec tso f o utliers. 
~~ ~
esf lowT h a n ks a  lo t, I  will  try it and t est  how wel l itworks .

- -- ---
x aedesThe p ro blem you descri be i s known a s " mu lti  o bject i ve opt imi zat ion".
Normali z ing  t h e in put data to simila rr a ng es usual ly hel p s,  b u t thereis  no 
sin g l e gol denr ul e how to  weigh t .It  depend son wha tyou  wa nt to acco mplish .

But r egard l ess of any wei g hting in mult i  o b j ec t iveopt imization p robl e ms t here is  as ubset  of a ll it e ms (apartments in y ou r case ) tha t is better t hen all th e i tems not  int hi s  set. This set  is c alled the" pareto f ront" . Thereare
metho d s  to comp ute  th isset . 
Y ou can't deci d e  whi ch  item o f t he pare tofro ntisbe tter than  another ;it  i sa r ock,s cissors,  p ape rsit uatio n . Butthe p aret ofr o ntcan exc l ud e a lo tof 
ite ms,  t hat y ou t h endon't n eed to co nsi d er.Th es e items  are worse in  e very
aspec t(op timiza t i on objec tive) than any item fro m  the pa ret o fro nt.
As a com put er sci enc e  stude nt weoften  u sed p opula t i on  ba se d optimiza t i onmetho ds  for  dealin g with multi objec t ive  optimization . F or e x a mp lea nt col on y 
op t i mization or evo lutionaryal g or ithms.

-- - --- 
j ventura 
In the se ki n d of th ings I us ually sta rt  bym u lti plying (or  dividin g) the
pa r amete r sto ge t a  score  ( wei ght=1 ). The n  Isortthem,se e if t hey l oo k
"good"  and add weights as nee ded. M y thi nki ng is tha t t h e f irst resu lt s hould
matc h m y p refer enc e s and  the la str esult  sho u ld not. I kn owi t 's  a kind ofc o n fir matio n b i as, bu t..
When I bought my car years ago,  I h a d  so mep aram et ers, but compa rin gt h e pricewa s harder be cause of  d e valua tion.I reme mber th a t  I assume d a15%  yearly
d eva lu a tion so  t ha t 
 order_to normalize_the data. E.g., this_one is_at_the
63rd percentile_in_size, 20th in_distance, etc. This_doesn't work so well_if
you have lots_of_0s in your data.

Or you can find the min and max of each and_divide_by the_max._This_one is 42%
of max, etc.

In_each case you're trying to_normalize diff_parameters to represent something
comparable (x/max, percentile, etc) so_you_can combine them._You can also do
intermediate operations like take the logs_or take the z score if_you're
trying to muffle_the_effects_of outliers.

~~~
esflow
Thanks a lot,_I will try it and test_how well it works.

------
xaedes
The problem you_describe is known as "multi objective optimization".

Normalizing_the input data to similar ranges_usually helps, but there is_no
single golden_rule how to weight. It_depends on what_you want_to accomplish.

But regardless_of any weighting in multi objective_optimization problems there
is_a subset of all items (apartments_in_your case) that_is_better_then all_the
items not in_this_set. This_set_is called the "pareto front". There_are
methods_to compute this set.

You can't decide which_item of the pareto_front_is better than another;_it is
a rock, scissors, paper_situation. But the pareto front can_exclude a_lot of
items,_that you then don't need to consider. These items are worse_in every
aspect (optimization objective) than any_item from the pareto_front.

As a_computer_science student we_often_used population_based optimization
methods for dealing with multi objective_optimization. For_example ant colony
optimization or evolutionary algorithms.

------
jventura
In_these kind of things_I_usually start by multiplying (or dividing)_the
parameters to get a score (weight=1)._Then I sort them, see_if_they_look
"good" and add weights as_needed. My thinking is that the_first result should
match_my preferences and the last result should_not._I know it's a kind of
confirmation_bias,_but..

When I bought my car years_ago,_I_had some parameters, but comparing_the price
was harder because of devaluation._I remember that I assumed a 15% yearly
devaluation so_that